Table of Contents
*****************

UNU.RAN - Universal Non-Uniform RANdom number generators
1 Introduction
  1.1 Usage of this document
  1.2 Installation
  1.3 Using the library
  1.4 Concepts of UNU.RAN
  1.5 Contact the authors
2 Examples
  2.1 As short as possible
  2.2 As short as possible (String API)
  2.3 Select a method
  2.4 Select a method (String API)
  2.5 Arbitrary distributions
  2.6 Arbitrary distributions (String API)
  2.7 Change parameters of the method
  2.8 Change parameters of the method (String API)
  2.9 Change uniform random generator
  2.10 Sample pairs of antithetic random variates
  2.11 Sample pairs of antithetic random variates (String API)
  2.12 More examples
3 String Interface
  3.1 Syntax of String Interface
  3.2 Distribution String
    3.2.1 Keys for Distribution String
  3.3 Function String
  3.4 Method String
    3.4.1 Keys for Method String
  3.5 Uniform RNG String
4 Handling distribution objects
  4.1 Functions for all kinds of distribution objects
  4.2 Continuous univariate distributions
  4.3 Continuous univariate order statistics
  4.4 Continuous empirical univariate distributions
  4.5 Continuous multivariate distributions
  4.6 Continuous univariate full conditional distribution
  4.7 Continuous empirical multivariate distributions
  4.8 MATRix distributions
  4.9 Discrete univariate distributions
5 Methods for generating non-uniform random variates
  5.1 Routines for all generator objects
  5.2 AUTO  -  Select method automatically
  5.3 Methods for continuous univariate distributions
    5.3.1 AROU  -  Automatic Ratio-Of-Uniforms method
    5.3.2 ARS  -  Adaptive Rejection Sampling
    5.3.3 CEXT  -  wrapper for Continuous EXTernal generators
    5.3.4 CSTD  -  Continuous STandarD distributions
    5.3.5 HINV  -  Hermite interpolation based INVersion of CDF
    5.3.6 HRB  -  Hazard Rate Bounded
    5.3.7 HRD  -  Hazard Rate Decreasing
    5.3.8 HRI  -  Hazard Rate Increasing
    5.3.9 ITDR  -  Inverse Transformed Density Rejection
    5.3.10 NINV  -  Numerical INVersion
    5.3.11 NROU  -  Naive Ratio-Of-Uniforms method
    5.3.12 PINV  -  Polynomial interpolation based INVersion of CDF
    5.3.13 SROU  -  Simple Ratio-Of-Uniforms method
    5.3.14 SSR  -  Simple Setup Rejection
    5.3.15 TABL  -  a TABLe method with piecewise constant hats
    5.3.16 TDR  -  Transformed Density Rejection
    5.3.17 UTDR  -  Universal Transformed Density Rejection
  5.4 Methods for continuous empirical univariate distributions
    5.4.1 EMPK  -  EMPirical distribution with Kernel smoothing
    5.4.2 EMPL  -  EMPirical distribution with Linear interpolation
    5.4.3 HIST  -  HISTogramm of empirical distribution
  5.5 Methods for continuous multivariate distributions
    5.5.1 MVSTD  -  MultiVariate continuous STandarD distributions
    5.5.2 MVTDR  -  Multi-Variate Transformed Density Rejection
    5.5.3 NORTA  -  NORmal To Anything
    5.5.4 VNROU  -  Multivariate Naive Ratio-Of-Uniforms method
  5.6 Markov chain samplers for continuous multivariate distributions
    5.6.1 GIBBS  -  Markov Chain - GIBBS sampler
    5.6.2 HITRO  -  Markov Chain - HIT-and-run sampler with Ratio-Of-uniforms
  5.7 Methods for continuous empirical multivariate distributions
    5.7.1 VEMPK  -  (Vector) EMPirical distribution with Kernel smoothing
  5.8 Methods for discrete univariate distributions
    5.8.1 DARI  -  Discrete Automatic Rejection Inversion
    5.8.2 DAU  -  (Discrete) Alias-Urn method
    5.8.3 DEXT  -  wrapper for Discrete EXTernal generators
    5.8.4 DGT  -  (Discrete) Guide Table method (indexed search)
    5.8.5 DSROU  -  Discrete Simple Ratio-Of-Uniforms method
    5.8.6 DSS  -  (Discrete) Sequential Search method
    5.8.7 DSTD  -  Discrete STandarD distributions
  5.9 Methods for random matrices
    5.9.1 MCORR  -  Random CORRelation matrix
  5.10 Methods for uniform univariate distributions
    5.10.1 UNIF  -  wrapper for UNIForm random number generator
  5.11 Meta Methods for univariate distributions
    5.11.1 MIXT  -  MIXTure of distributions
6 Using uniform random number generators
  6.1 Simple interface for uniform random number generators
  6.2 Interface to GSL uniform random number generators
  6.3 Interface to GSL generators for quasi-random points
  6.4 Interface to Otmar Lendl's pseudo-random number generators
  6.5 Interface to L'Ecuyer's RNGSTREAM random number generators
  6.6 Combine point set generator with random shifts
7 UNU.RAN Library of standard distributions
  7.1 UNU.RAN Library of continuous univariate distributions
    7.1.1 `F'  -  F-distribution
    7.1.2 `beta'  -  Beta distribution
    7.1.3 `cauchy'  -  Cauchy distribution
    7.1.4 `chi'  -  Chi distribution
    7.1.5 `chisquare'  -  Chisquare distribution
    7.1.6 `exponential'  -  Exponential distribution
    7.1.7 `extremeI'  -  Extreme value type I (Gumbel-type) distribution
    7.1.8 `extremeII'  -  Extreme value type II (Frechet-type) distribution
    7.1.9 `gamma'  -  Gamma distribution
    7.1.10 `gig'  -  Generalized Inverse Gaussian distribution
    7.1.11 `gig2'  -  Generalized Inverse Gaussian distribution
    7.1.12 `hyperbolic'  -  Hyperbolic distribution
    7.1.13 `ig'  -  Inverse Gaussian distribution
    7.1.14 `laplace'  -  Laplace distribution
    7.1.15 `logistic'  -  Logistic distribution
    7.1.16 `lognormal'  -  Log-Normal distribution
    7.1.17 `lomax'  -  Lomax distribution (Pareto distribution of second kind)
    7.1.18 `normal'  -  Normal distribution
    7.1.19 `pareto'  -  Pareto distribution (of first kind)
    7.1.20 `powerexponential'  -  Powerexponential (Subbotin) distribution
    7.1.21 `rayleigh'  -  Rayleigh distribution
    7.1.22 `slash'  -  Slash distribution
    7.1.23 `student'  -  Student's t distribution
    7.1.24 `triangular'  -  Triangular distribution
    7.1.25 `uniform'  -  Uniform distribution
    7.1.26 `weibull'  -  Weibull distribution
  7.2 UNU.RAN Library of continuous multivariate distributions
    7.2.1 `copula'  -  Copula (distribution with uniform marginals)
    7.2.2 `multicauchy'  -  Multicauchy distribution
    7.2.3 `multiexponential'  -  Multiexponential distribution
    7.2.4 `multinormal'  -  Multinormal distribution
    7.2.5 `multistudent'  -  Multistudent distribution
  7.3 UNU.RAN Library of discrete univariate distributions
    7.3.1 `binomial'  -  Binomial distribution
    7.3.2 `geometric'  -  Geometric distribution
    7.3.3 `hypergeometric'  -  Hypergeometric distribution
    7.3.4 `logarithmic'  -  Logarithmic distribution
    7.3.5 `negativebinomial'  -  Negative Binomial distribution
    7.3.6 `poisson'  -  Poisson distribution
  7.4 UNU.RAN Library of random matrices
    7.4.1 `correlation'  -  Random correlation matrix
8 Error handling and Debugging
  8.1 Output streams
  8.2 Debugging
  8.3 Error reporting
  8.4 Error codes
  8.5 Error handlers
9 Testing
10 Miscelleanous
  10.1 Mathematics
Appendix A A Short Introduction to Random Variate Generation
  A.1 The Inversion Method
  A.2 The Rejection Method
  A.3 The Composition Method
  A.4 The Ratio-of-Uniforms Method
  A.5 Inversion for Discrete Distributions
  A.6 Indexed Search (Guide Table Method)
Appendix B Glossary
Appendix C Bibliography
Appendix D Function Index


UNU.RAN - Universal Non-Uniform RANdom number generators
********************************************************

This is the online-documentation of UNU.RAN.
Version: 1.8.1
Date: 19 April 2011

UNU.RAN (Universal Non-Uniform RAndom Number generator) is a collection of
algorithms for generating non-uniform pseudorandom variates as a library of C
functions designed and implemented by the ARVAG (Automatic Random VAriate
Generation) project group in Vienna, and released under the GNU Public License
(GPL).  It is especially designed for such situations where

   - a non-standard distribution or a truncated distribution is needed.

   - experiments with different types of distributions are made.

   - random variates for variance reduction techniques are used.

   - fast generators of predictable quality are necessary.


   Of course it is also well suited for standard distributions.  However due to
its more sophisticated programming interface it might not be as easy to use if
you only look for a generator for the standard normal distribution. (Although
UNU.RAN provides generators that are superior in many aspects to those found in
quite a number of other libraries.)

   UNU.RAN implements several methods for generating random numbers.  The
choice depends primary on the information about the distribution can be
provided and - if the user is familar with the different methods - on the
preferences of the user.

   The design goals of UNU.RAN are to provide _reliable_, _portable_ and
_robust_ (as far as this is possible) functions with a consisent and easy to
use interface. It is suitable for all situation where experiments with different
distributions including non-standard distributions.  For example it is no
problem to replace the normal distribution by an empirical distribution in a
model.

   Since originally designed as a library for so called black-box or universal
algorithms its interface is different from other libraries. (Nevertheless it
also contains special generators for standard distributions.) It does not
provide subroutines for random variate generation for particular distributions.
Instead it uses an object-oriented interface. Distributions and generators are
treated as independent objects. This approach allows one not only to have
different methods for generating non-uniform random variates. It is also
possible to choose the method which is optimal for a given situation (e.g.
speed, quality of random numbers, using for variance reduction techniques,
etc.). It also allows to sample from non-standard distribution or even from
distributions that arise in a model and can only be computed in a complicated
subroutine.

   Sampling from a particular distribution requires the following steps:

  1. Create a distribution object.  (Objects for standard distributions are
     available in the library)

  2. Choose a method.

  3. Initialize the generator, i.e., create the generator object.  If the
     choosen method is not suitable for the given distribution (or if the
     distribution object contains too little information about the
     distribution) the initialization routine fails and produces an error
     message. Thus the generator object does (probably) not produce false
     results (random variates of a different distribution).

  4. Use this generator object to sample from the distribution.


   There are four types of objects that can be manipulated independently:

   * *Distribution objects:* hold all information about the random variates
     that should be generated. The following types of distributions are
     available:

        - Continuous and Discrete distributions

        - Empirical distributions

        - Multivariate distributions

     Of course a library of standard distributions is included (and these can
     be further modified to get, e.g., truncated distributions). Moreover the
     library provides subroutines to build almost arbitrary distributions.

   * *Generator objects:* hold the generators for the given distributions. It
     is possible to build independent generator objects for the same
     distribution object which might use the same or different methods for
     generation. (If the choosen method is not suitable for the given method, a
     `NULL' pointer is returned in the initialization step).

   * *Parameter objects:* Each transformation method requires several
     parameters to adjust the generator to a given distribution. The parameter
     object holds all this information. When created it contains all necessary
     default settings. It is only used to create a generator object and
     destroyed immediately. Altough there is no need to change these parameters
     or even know about their existence for "usual distributions", they allow a
     fine tuning of the generator to work with distributions with some awkward
     properties. The library provides all necessary functions to change these
     default parameters.

   * *Uniform Random Number Generators:* All generator objects need one (or
     more) streams of uniform random numbers that are transformed into random
     variates of the given distribution. These are given as pointers to
     appropriate functions or structures (objects). Two generator objects may
     have their own uniform random number generators or share a common one. Any
     functions that produce uniform (pseudo-) random numbers can be used. We
     suggest Otmar Lendl's PRNG library.


1 Introduction
**************

1.1 Usage of this document
==========================

We designed this document in a way such that one can use UNU.RAN with reading
as little as necessary.  Read *note Installation:: for the instructions to
install the library.  *note Concepts of UNU.RAN: Concepts, discribes the basics
of UNU.RAN.  It also has a short guideline for choosing an appropriate method.
In *note Examples:: examples are given that can be copied and modified.  They
also can be found in the directory `examples' in the source tree.

   Further information are given in consecutive chapters.  *note Handling
distribution objects: Distribution_objects, describes how to create and
manipulate distribution objects.  *note standard distributions: Stddist,
describes predefined distribution objects that are ready to use.  *note
Methods:: describes the various methods in detail.  For each of possible
distribution classes (continuous, discrete, empirical, multivariate) there
exists a short overview section that can be used to choose an appropriate
method followed by sections that describe each of the particular methods in
detail.  These are merely for users with some knowledge about the methods who
want to change method-specific parameters and can be ignored by others.

   Abbreviations and explanation of some basic terms can be found in *note
Glossary::.

1.2 Installation
================

UNU.RAN was developed on an Intel architecture under Linux with the GNU C
compiler but should compile and run on any computing environment. It requires
an ANSI compliant C compiler.

   Below find the installation instructions for unices.

Uniform random number generator
...............................

UNU.RAN can be used with any uniform random number generator but (at the
moment) some features work best with Pierre L'Ecuyer's RngStreams library (see
`http://statmath.wu.ac.at/software/RngStreams/' for a description and
downloading.  For details on using uniform random number in UNU.RAN see *note
Using uniform random number generators: URNG.

   Install the required libraries first.

UNU.RAN
.......

  1. First unzip and untar the package and change to the directory:
          tar zxvf unuran-1.8.1.tar.gz
          cd unuran-1.8.1

  2. Optional: Edit the file `src/unuran_config.h'

  3. Run a configuration script:
          sh ./configure --prefix=<prefix>

     where `<prefix>' is the root of the installation tree.  When omitted
     `/usr/local' is used.

     Use `./configure --help' to get a list of other options.  In particular
     the following flags are important:

        * Enable support for some external sources of uniform random number
          generators (*note Using uniform random number generators: URNG.):

         `--with-urng-rngstream'
               URNG: use Pierre L'Ecuyer's RNGSTREAM library [default=`no']

         `--with-urng-prng'
               URNG: use Otmar Lendl's PRNG library [default=`no']

         `--with-urng-gsl'
               URNG: use random number generators from GNU Scientific Library
               [default=`no']

         `--with-urng-default'
               URNG: global default URNG (builtin|rngstream) [default=`builtin']

          We strongly recommend to use RngStreams library:
               sh ./configure --with-urng-rngstream --with-urng-default=rngstream

          _Important:_ You must install the respective libraries `RngStreams',
          `PRNG' and `GSL' before `./configure' is executed.

        * Also make a shared library:

         `--enable-shared'
               build shared libraries  [default=`no']

        * The library provides the function `unur_gen_info' for information
          about generator objects. This is intented for using in interactive
          computing environments.  This feature can be enabled / disabled by
          means of the configure flag

         `--enable-info'
               INFO: provide function with information about generator objects
               [default=`yes']

        * Enable support for deprecated UNU.RAN routines if you have some
          problems with older application after upgrading the library:

         `--enable-deprecated'
               enable support for deprecated UNU.RAN routines [default=`no']

        * Enable debugging tools:

         `--enable-check-struct'
               Debug: check validity of pointers to structures [default=`no']

         `--enable-logging'
               Debug: print informations about generator into logfile
               [default=no]

  4. Compile and install the libray:
          make
          make install

     Obviously `$(prefix)/include' and `$(prefix)/lib' must be in the search
     path of your compiler. You can use environment variables to add these
     directories to the search path. If you are using the bash type (or add to
     your profile):
          export LIBRARY_PATH="<prefix>/lib"
          export C_INCLURE_PATH="<prefix>/include"

     If you want to make a shared library, then making such a library can be
     enabled using
          sh ./configure --enable-shared
     If you want to link against the shared library make sure that it can be
     found when executing the binary that links to the library. If it is not
     installed in the usual path, then the easiest way is to set the
     `LD_LIBRARY_PATH' environment variable. See any operating system
     documentation about shared libraries for more information, such as the
     ld(1) and ld.so(8) manual pages.

  5. Documentation in various formats (PDF, HTML, info, plain text) can be
     found in directory `doc'.

  6. You can run some tests by
          make check

     However, some of these tests requires the usage of the PRNG or RngStreams
     library and are only executed if these are installed enabled by the
     corresponding configure flag.

     An extended set of tests is run by
          make fullcheck

     However some of these might fail occasionally due to roundoff errors or
     the mysteries of floating point arithmetic, since we have used some
     extreme settings to test the library.


Upgrading
.........

   - _Important:_

     UNU.RAN now relies on some aspects of IEEE 754 compliant floating point
     arithmetic. In particular, `1./0.' and `0./0.' must result in `infinity'
     and `NaN' (not a number), respectively, and must not cause a floating
     point exception.  For allmost all modern compting architecture this is
     implemented in hardware. For others there should be a special compiler flag
     to get this feature (e.g., `-MIEEE' on DEC alpha or `-mp' for the Intel C
     complier).

   - Upgrading UNU.RAN from version 0.9.x or earlier:

     With UNU.RAN version 1.0.x some of the macro definitions in file
     `src/unuran_config.h' are moved into file `config.h' and are
     set/controlled by the `./configure' script.

     Writting logging information into the logfile must now be enabled when
     running the configure script:
          sh ./configure --enable-logging

   - Upgrading UNU.RAN from version 0.7.x or earlier:

     With UNU.RAN version 0.8.0 the interface for changing underlying
     distributions and running a reinitialization routine has been simplified.
     The old routines can be compiled into the library using the following
     configure flag:
          sh ./configure --enable-deprecated

     Notice: Using these deprecated routines is not supported any more and this
     strong discouraged.

     Wrapper functions for external sources of uniform random numbers are now
     enabled by configure flags and not by macros defined in file
     `src/unuran_config.h'.

     The file `src/unuran_config.h' is not installed any more. It is now only
     included when the library is compiled.  It should be removed from the
     global include path of the compiler.


1.3 Using the library
=====================

ANSI C Compliance
.................

The library is written in ANSI C and is intended to conform to the ANSI C
standard.  It should be portable to any system with a working ANSI C compiler.

   The library does not rely on any non-ANSI extensions in the interface it
exports to the user.  Programs you write using UNU.RAN can be ANSI compliant.
Extensions which can be used in a way compatible with pure ANSI C are
supported, however, via conditional compilation.	 This allows the
library to take advantage of compiler extensions on those platforms which
support them.

   To avoid namespace conflicts all exported function names and variables have
the prefix `unur_', while exported macros have the prefix `UNUR_'.

Compiling and Linking
.....................

If you want to use the library you must include the UNU.RAN header file
     #include <unuran.h>
   If you also need the test routines then also add
     #include <unuran_tests.h>
   If wrapper functions for external sources of uniform random number
generators are used, the corresponding header files must also be included, e.g.,
     #include <unuran_urng_rngstream.h>

If these header files are not installed on the standard search path of your
compiler you will also need to provide its location to the preprocessor as a
command line flag.	 The default location of the `unuran.h' is
`/usr/local/include'.  A typical compilation command for a source file `app.c'
with the GNU C compiler `gcc' is,

     gcc -I/usr/local/include -c app.c

This results in an object file `app.o'.	 The default include path for `gcc'
searches `/usr/local/include' automatically so the `-I' option can be omitted
when UNU.RAN is installed in its default location.

   The library is installed as a single file, `libunuran.a'.  A shared version
of the library is also installed on systems that support shared libraries.  The
default location of these files is `/usr/local/lib'.  To link against the
library you need to specify the main library.  The following example shows how
to link an application with the library (and the the RNGSTREAMS library if you
decide to use this source of uniform pseudo-random numbers),

     gcc app.o -lunuran -lrngstreams -lm

Shared Libraries
................

To run a program linked with the shared version of the library it may be
necessary to define the shell variable `LD_LIBRARY_PATH' to include the
directory where the library is installed.  For example,

     LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
   To compile a statically linked version of the program instead, use the
`-static' flag in `gcc',

     gcc -static app.o -lunuran -lrngstreams -lm

Compatibility with C++
......................

The library header files automatically define functions to have `extern "C"'
linkage when included in C++ programs.

1.4 Concepts of UNU.RAN
=======================

UNU.RAN is a C library for generating non-uniformly distributed random
variates. Its emphasis is on the generation of non-standard distribution and on
streams of random variates of special purposes.  It is designed to provide a
consistent tool to sample from distributions with various properties.  Since
there is no universal method that fits for all situations, various methods for
sampling are implemented.

   UNU.RAN solves this complex task by means of an object oriented programming
interface. Three basic objects are used:

   * distribution object `UNUR_DISTR'
     Hold all information about the random variates that should be generated.

   * generator object `UNUR_GEN'
     Hold the generators for the given distributions.  Two generator objects
     are completely independent of each other.  They may share a common uniform
     random number generator or have their owns.

   * parameter object `UNUR_PAR'
     Hold all information for creating a generator object. It is necessary due
     to various parameters and switches for each of these generation methods.

     Notice that the parameter objects only hold pointers to arrays but do not
     have their own copy of such an array.  Especially, if a dynamically
     allocated array is used it _must not_ be freed until the generator object
     has been created!


   The idea behind these structures is that creatin distributions, choosing a
generation method and draing samples are orthogonal (ie. independent) functions
of the library.  The parameter object is only introduced due to the necessity to
deal with various parameters and switches for each of these generation methods
which are required to adjust the algorithms to unusual distributions with
extreme properties but have default values that are suitable for most
applications.  These parameters and the data for distributions are set by
various functions.

   Once a generator object has been created sampling (from the univariate
continuous distribution) can be done by the following command:
     double x = unur_sample_cont(generator);
   Analogous commands exist for discrete and multivariate distributions.  For
detailed examples that can be copied and modified see *note Examples::.

Distribution objects
--------------------

All information about a distribution are stored in objects (structures) of type
`UNUR_DISTR'.  UNU.RAN has five different types of distribution objects:

`cont'
     Continuous univariate distributions.

`cvec'
     Continuous multivariate distributions.

`discr'
     Discrete univariate distributions.

`cemp'
     Continuous empirical univariate distribution, ie. given by a sample.

`cvemp'
     Continuous empirical multivariate distribution, ie. given by a sample.

`matr'
     Matrix distributions.


Distribution objects can be created from scratch by the following call
     distr = unur_distr_<type>_new();
   where `<type>' is one of the five possible types from the above table.
Notice that these commands only create an _empty_ object which still must be
filled by means of calls for each type of distribution object (*note Handling
distribution objects: Distribution_objects.).  The naming scheme of these
functions is designed to indicate the corresponding type of the distribution
object and the task to be performed. It is demonstated on the following example.
     unur_distr_cont_set_pdf(distr, mypdf);
   This command stores a PDF named `mypdf' in the distribution object `distr'
which must have the type `cont'.

   Of course UNU.RAN provides an easier way to use standard distributions.
Instead of using `unur_distr_<type>_new' calls and fuctions
`unur_distr_<type>_set_<...>' for setting data, objects for standard
distribution can be created by a single call.  Eg. to get an object for the
normal distribution with mean 2 and standard deviation 5 use
     double parameter[2] = {2.0 ,5.0};
     UNUR_DISTR *distr = unur_distr_normal(parameter, 2);
   For a list of standard distributions see *note Standard distributions:
Stddist.

Generation methods
------------------

The information that a distribution object must contain depends heavily on the
chosen generation method choosen.

   Brackets indicate optional information while a tilde indicates that only an
approximation must be provided.  See *note Glossary::, for unfamiliar terms.


Methods for continuous univariate distributions
sample with `unur_sample_cont'

method   PDF   dPDF   CDF   mode   area   other
AROU     x     x            [x]           T-concave
HINV     [x]   [x]    x                   
HRB                                       bounded hazard rate
HRD                                       decreasing hazard rate
HRI                                       increasing hazard rate
ITDR     x     x            x             monotone with pole
NINV     [x]          x                   
NROU     x                  [x]           
SROU     x                  x      x      T-concave
SSR      x                  x      x      T-concave
TABL     x                  x      [~]    all local extrema
TDR      x     x                          T-concave
TDRGW    x     x                          T-concave
UTDR     x                  x      ~      T-concave
CSTD                                      build-in standard distribution
CEXT                                      wrapper for external generator


Methods for continuous empirical univariate distributions
sample with `unur_sample_cont'

EMPK:  Requires an observed sample.
EMPL:  Requires an observed sample.


Methods for continuous multivariate distributions
sample with `unur_sample_vec'

NORTA: Requires rank correlation matrix and marginal distributions.
VNROU: Requires the PDF.
MVSTD: Generator for built-in standard distributions.
MVTDR: Requires PDF and gradiant of PDF.


Methods for continuous empirical multivariate distributions
sample with `unur_sample_vec'

VEMPK: Requires an observed sample.


Methods for discrete univariate distributions
sample with `unur_sample_discr'

method   PMF   PV   mode   sum    other
DARI     x          x      ~      T-concave
DAU      [x]   x                  
DGT      [x]   x                  
DSROU    x          x      x      T-concave
DSS      [x]   x           x      
DSTD                              build-in standard distribution
CEXT                              wrapper for external generator


Methods for matrix distributions
sample with `unur_sample_matr'

MCORR: Distribution object for random correlation matrix.


Markov Chain Methods for continuous multivariate distributions
sample with `unur_sample_vec'

GIBBS: T-concave logPDF and derivatives of logPDF.
HITRO: Requires PDF.



   Because of tremendous variety of possible problems, UNU.RAN provides many
methods. All information for creating a generator object has to be collected in
a parameter object first.  For example, if the task is to sample from a
continuous distribution the method AROU might be a good choice. Then the call
     UNUR_PAR *par = unur_arou_new(distribution);
   creates an parameter object `par' with a pointer to the distribution object
and default values for all necessary parameters for method AROU.  Other methods
can be used by replacing `arou' with the name of the desired methods (in lower
case letters):
     UNUR_PAR *par = unur_<method>_new(distribution);
   This sets the default values for all necessary parameters for the chosen
method. These are suitable for almost all applications. Nevertheless, it is
possible to control the behavior of the method using corresponding `set' calls
for each method.  This might be necessary to adjust the algorithm for an unusual
distribution with extreme properties, or just for fine tuning the perforence of
the algorithm.  The following example demonstrates how to change the maximum
number of iterations for method NINV to the value 50:
     unur_ninv_set_max_iteration(par, 50);
   All available methods are described in details in *note Methods::.

Creating a generator object
---------------------------

Now it is possible to create a generator object:
     UNUR_GEN *generator = unur_init(par);
     if (generator == `NULL') exit(EXIT_FAILURE);

*Important:* You must always check whether `unur_init' has been executed
successfully. Otherwise the `NULL' pointer is returned which causes a
segmentation fault when used for sampling.

*Important:* The call of `unur_init' *destroys* the parameter object!
Moreover, it is recommended to call `unur_init' immediately after the parameter
object `par' has created and modified.


   An existing generator object is a rather static construct.  Nevertheless,
some of the parameters can still be modified by `chg' calls, e.g.
     unur_ninv_chg_max_iteration(gen, 30);


   Notice that it is important _when_ parameters are changed because different
functions must be used:

   The function name includes the term `set' and the first argument must be of
type `UNUR_PAR' when the parameters are changed _before_ the generator object
is created.

   The function name includes the term `chg' and the first argument must be of
type `UNUR_GEN' when the parameters are changed for an _existing_ generator
object.

   For details see *note Methods::.

Sampling
--------

You can now use your generator object in any place of your program to sample
from your distribution. You only have to take care about the type of variates
it computes: `double', `int' or a vector (array of `double's).  Notice that at
this point it does not matter whether you are sampling from a gamma
distribution, a truncated normal distribution or even an empirical distribution.

Reinitializing
--------------

It is possible for a generator object to change the parameters and the domain
of the underlying distribution. This must be done by extracting this object by
means of a `unur_get_distr' call and changing the distribution using the
correspondig set calls, see *note Handling distribution objects:
Distribution_objects.  The generator object *must* then be reinitialized by
means of the `unur_reinit' call.

   _Important_: Currently not all methods allow reinitialization, see the
description of the particular method (keyword Reinit).

Destroy
-------

When you do not need your generator object any more, you should destroy it:
     unur_free(generator);

Uniform random numbers
----------------------

Each generator object can have its own uniform random number generator or share
one with others.  When created a parameter object the pointer for the uniform
random number generator is set to the default generator. However, it can be
changed at any time to any other generator:
     unur_set_urng(par, urng);
   or
     unur_chg_urng(generator, urng);
   respectively.  See *note Using uniform random number generators: URNG, for
details.

1.5 Contact the authors
=======================

If you have any problems with UNU.RAN, suggestions how to improve the library,
or find a bug, please contact us via email <unuran@statmath.wu.ac.at>.

   For news please visit out homepage at `http://statmath.wu.ac.at/unuran/'.

2 Examples
**********

The examples in this chapter should compile cleanly and can be found in the
directory `examples' of the source tree of UNU.RAN. Assuming that UNU.RAN as
well as the PRNG libraries have been installed properly (*note Installation::)
each of these can be compiled (using the GCC in this example) with
     gcc -Wall -O2 -o example example.c -lunuran -lprng -lm
   _Remark:_ `-lprng' must be omitted when the PRNG library is not installed.
Then however some of the examples might not work.

   The library uses three objects: `UNUR_DISTR', `UNUR_PAR' and `UNUR_GEN'.  It
is not important to understand the details of these objects but it is important
not to changed the order of their creation.  The distribution object can be
destroyed _after_ the generator object has been made. (The parameter object is
freed automatically by the `unur_init' call.) It is also important to check the
result of the `unur_init' call. If it has failed the `NULL' pointer is returned
and causes a segmentation fault when used for sampling.

   We give all examples with the UNU.RAN standard API and the more convenient
string API.

2.1 As short as possible
========================

Select a distribution and let UNU.RAN do all necessary steps.

     /* ------------------------------------------------------------- */
     /* File: example0.c                                              */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;     /* loop variable                                 */
       double x;     /* will hold the random number                   */

       /* Declare the three UNURAN objects.                           */
       UNUR_DISTR *distr;    /* distribution object                   */
       UNUR_PAR   *par;      /* parameter object                      */
       UNUR_GEN   *gen;      /* generator object                      */

       /* Use a predefined standard distribution:                     */
       /*   Gaussian with mean zero and standard deviation 1.         */
       /*   Since this is the standard form of the distribution,      */
       /*   we need not give these parameters.                        */
       distr = unur_distr_normal(NULL, 0);

       /* Use method AUTO:                                            */
       /*   Let UNURAN select a suitable method for you.              */
       par = unur_auto_new(distr);

       /* Now you can change some of the default settings for the     */
       /* parameters of the chosen method. We don't do it here.       */

       /* Create the generator object.                                */
       gen = unur_init(par);

       /* Notice that this call has also destroyed the parameter      */
       /* object `par' as a side effect.                              */

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* It is possible to reuse the distribution object to create   */
       /* another generator object. If you do not need it any more,   */
       /* it should be destroyed to free memory.                      */
       unur_distr_free(distr);

       /* Now you can use the generator object `gen' to sample from   */
       /* the standard Gaussian distribution.                         */
       /* Eg.:                                                        */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* When you do not need the generator object any more, you     */
       /* can destroy it.                                             */
       unur_free(gen);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

2.2 As short as possible (String API)
=====================================

Select a distribution and let UNU.RAN do all necessary steps.

     /* ------------------------------------------------------------- */
     /* File: example0_str.c                                          */
     /* ------------------------------------------------------------- */
     /* String API.                                                   */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;     /* loop variable                                 */
       double x;     /* will hold the random number                   */

       /* Declare UNURAN generator object.                            */
       UNUR_GEN   *gen;      /* generator object                      */

       /* Create the generator object.                                */
       /* Use a predefined standard distribution:                     */
       /*   Standard Gaussian distribution.                           */
       /* Use method AUTO:                                            */
       /*   Let UNURAN select a suitable method for you.              */
       gen = unur_str2gen("normal()");

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* Now you can use the generator object `gen' to sample from   */
       /* the standard Gaussian distribution.                         */
       /* Eg.:                                                        */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* When you do not need the generator object any more, you     */
       /* can destroy it.                                             */
       unur_free(gen);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

2.3 Select a method
===================

Select method AROU and use it with default parameters.

     /* ------------------------------------------------------------- */
     /* File: example1.c                                              */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;     /* loop variable                                 */
       double x;     /* will hold the random number                   */

       /* Declare the three UNURAN objects.                           */
       UNUR_DISTR *distr;    /* distribution object                   */
       UNUR_PAR   *par;      /* parameter object                      */
       UNUR_GEN   *gen;      /* generator object                      */

       /* Use a predefined standard distribution:                     */
       /*   Gaussian with mean zero and standard deviation 1.         */
       /*   Since this is the standard form of the distribution,      */
       /*   we need not give these parameters.                        */
       distr = unur_distr_normal(NULL, 0);

       /* Choose a method: AROU.                                      */
       /* For other (suitable) methods replace "arou" with the        */
       /* respective name (in lower case letters).                    */
       par = unur_arou_new(distr);

       /* Now you can change some of the default settings for the     */
       /* parameters of the chosen method. We don't do it here.       */

       /* Create the generator object.                                */
       gen = unur_init(par);

       /* Notice that this call has also destroyed the parameter      */
       /* object `par' as a side effect.                              */

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* It is possible to reuse the distribution object to create   */
       /* another generator object. If you do not need it any more,   */
       /* it should be destroyed to free memory.                      */
       unur_distr_free(distr);

       /* Now you can use the generator object `gen' to sample from   */
       /* the standard Gaussian distribution.                         */
       /* Eg.:                                                        */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* When you do not need the generator object any more, you     */
       /* can destroy it.                                             */
       unur_free(gen);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

2.4 Select a method (String API)
================================

Select method AROU and use it with default parameters.

     /* ------------------------------------------------------------- */
     /* File: example1_str.c                                          */
     /* ------------------------------------------------------------- */
     /* String API.                                                   */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;     /* loop variable                                 */
       double x;     /* will hold the random number                   */

       /* Declare UNURAN generator object.                            */
       UNUR_GEN   *gen;      /* generator object                      */

       /* Create the generator object.                                */
       /* Use a predefined standard distribution:                     */
       /*   Standard Gaussian distribution.                           */
       /* Choose a method: AROU.                                      */
       /*   For other (suitable) methods replace "arou" with the      */
       /*   respective name.                                          */
       gen = unur_str2gen("normal() & method=arou");

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* Now you can use the generator object `gen' to sample from   */
       /* the standard Gaussian distribution.                         */
       /* Eg.:                                                        */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* When you do not need the generator object any more, you     */
       /* can destroy it.                                             */
       unur_free(gen);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

2.5 Arbitrary distributions
===========================

If you want to sample from a non-standard distribution, UNU.RAN might be
exactly what you need.  Depending on the information is available, a method
must be choosen for sampling, see *note Concepts:: for an overview and *note
Methods:: for details.

     /* ------------------------------------------------------------- */
     /* File: example2.c                                              */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */

     /* In this example we build a distribution object from scratch   */
     /* and sample from this distribution.                            */
     /*                                                               */
     /* We use method TDR (Transformed Density Rejection) which       */
     /* required a PDF and the derivative of the PDF.                 */

     /* ------------------------------------------------------------- */

     /* Define the PDF and dPDF of our distribution.                  */
     /*                                                               */
     /* Our distribution has the PDF                                  */
     /*                                                               */
     /*          /  1 - x*x  if |x| <= 1                              */
     /*  f(x) = <                                                     */
     /*          \  0        otherwise                                */
     /*                                                               */

     /* The PDF of our distribution:                                  */
     double mypdf( double x, const UNUR_DISTR *distr )
          /* The second argument (`distr') can be used for parameters */
          /* for the PDF. (We do not use parameters in our example.)  */
     {
       if (fabs(x) >= 1.)
         return 0.;
       else
         return (1.-x*x);
     } /* end of mypdf() */

     /* The derivative of the PDF of our distribution:                */
     double mydpdf( double x, const UNUR_DISTR *distr )
     {
       if (fabs(x) >= 1.)
         return 0.;
       else
         return (-2.*x);
     } /* end of mydpdf() */

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;     /* loop variable                                 */
       double x;     /* will hold the random number                   */

       /* Declare the three UNURAN objects.                           */
       UNUR_DISTR *distr;    /* distribution object                   */
       UNUR_PAR   *par;      /* parameter object                      */
       UNUR_GEN   *gen;      /* generator object                      */

       /* Create a new distribution object from scratch.              */
       /* It is a continuous distribution, and we need a PDF and the  */
       /* derivative of the PDF. Moreover we set the domain.          */

       /* Get empty distribution object for a continuous distribution */
       distr = unur_distr_cont_new();

       /* Assign the PDF and dPDF (defined above).                    */
       unur_distr_cont_set_pdf( distr, mypdf );
       unur_distr_cont_set_dpdf( distr, mydpdf );

       /* Set the domain of the distribution (optional for TDR).      */
       unur_distr_cont_set_domain( distr, -1., 1. );

       /* Choose a method: TDR.                                       */
       par = unur_tdr_new(distr);

       /* Now you can change some of the default settings for the     */
       /* parameters of the chosen method. We don't do it here.       */

       /* Create the generator object.                                */
       gen = unur_init(par);

       /* Notice that this call has also destroyed the parameter      */
       /* object `par' as a side effect.                              */

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* It is possible to reuse the distribution object to create   */
       /* another generator object. If you do not need it any more,   */
       /* it should be destroyed to free memory.                      */
       unur_distr_free(distr);

       /* Now you can use the generator object `gen' to sample from   */
       /* the distribution. Eg.:                                      */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* When you do not need the generator object any more, you     */
       /* can destroy it.                                             */
       unur_free(gen);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

2.6 Arbitrary distributions (String API)
========================================

If you want to sample from a non-standard distribution, UNU.RAN might be
exactly what you need.  Depending on the information is available, a method
must be choosen for sampling, see *note Concepts:: for an overview and *note
Methods:: for details.

     /* ------------------------------------------------------------- */
     /* File: example2_str.c                                          */
     /* ------------------------------------------------------------- */
     /* String API.                                                   */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */

     /* In this example we use a generic distribution object          */
     /* and sample from this distribution.                            */
     /*                                                               */
     /* The PDF of our distribution is given by                       */
     /*                                                               */
     /*          /  1 - x*x  if |x| <= 1                              */
     /*  f(x) = <                                                     */
     /*          \  0        otherwise                                */
     /*                                                               */
     /* We use method TDR (Transformed Density Rejection) which       */
     /* required a PDF and the derivative of the PDF.                 */

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;     /* loop variable                                 */
       double x;     /* will hold the random number                   */

       /* Declare UNURAN generator object.                            */
       UNUR_GEN   *gen;      /* generator object                      */

       /* Create the generator object.                                */
       /* Use a generic continuous distribution.                      */
       /* Choose a method: TDR.                                       */
       gen = unur_str2gen(
             "distr = cont; pdf=\"1-x*x\"; domain=(-1,1) & method=tdr");

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* Now you can use the generator object `gen' to sample from   */
       /* the distribution. Eg.:                                      */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* When you do not need the generator object any more, you     */
       /* can destroy it.                                             */
       unur_free(gen);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

2.7 Change parameters of the method
===================================

Each method for generating random numbers allows several parameters to be
modified. If you do not want to use default values, it is possible to change
them.  The following example illustrates how to change parameters.  For details
see *note Methods::.

     /* ------------------------------------------------------------- */
     /* File: example3.c                                              */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;          /* loop variable                            */
       double x;          /* will hold the random number              */

       double fparams[2]; /* array for parameters for distribution    */

       /* Declare the three UNURAN objects.                           */
       UNUR_DISTR *distr;    /* distribution object                   */
       UNUR_PAR   *par;      /* parameter object                      */
       UNUR_GEN   *gen;      /* generator object                      */

       /* Use a predefined standard distribution:                     */
       /*   Gaussian with mean 2. and standard deviation 0.5.         */
       fparams[0] = 2.;
       fparams[1] = 0.5;
       distr = unur_distr_normal( fparams, 2 );

       /* Choose a method: TDR.                                       */
       par = unur_tdr_new(distr);

       /* Change some of the default parameters.                      */

       /* We want to use T(x)=log(x) for the transformation.          */
       unur_tdr_set_c( par, 0. );

       /* We want to have the variant with immediate acceptance.      */
       unur_tdr_set_variant_ia( par );

       /* We want to use 10 construction points for the setup         */
       unur_tdr_set_cpoints ( par, 10, NULL );

       /* Create the generator object.                                */
       gen = unur_init(par);

       /* Notice that this call has also destroyed the parameter      */
       /* object `par' as a side effect.                              */

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* It is possible to reuse the distribution object to create   */
       /* another generator object. If you do not need it any more,   */
       /* it should be destroyed to free memory.                      */
       unur_distr_free(distr);

       /* Now you can use the generator object `gen' to sample from   */
       /* the distribution. Eg.:                                      */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* It is possible with method TDR to truncate the distribution */
       /* for an existing generator object ...                        */
       unur_tdr_chg_truncated( gen, -1., 0. );

       /* ... and sample again.                                       */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* When you do not need the generator object any more, you     */
       /* can destroy it.                                             */
       unur_free(gen);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

2.8 Change parameters of the method (String API)
================================================

Each method for generating random numbers allows several parameters to be
modified. If you do not want to use default values, it is possible to change
them.  The following example illustrates how to change parameters.  For details
see *note Methods::.

     /* ------------------------------------------------------------- */
     /* File: example3_str.c                                              */
     /* ------------------------------------------------------------- */
     /* String API.                                                   */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;          /* loop variable                            */
       double x;          /* will hold the random number              */

       /* Declare UNURAN generator object.                            */
       UNUR_GEN   *gen;      /* generator object                      */

       /* Create the generator object.                                */
       /* Use a predefined standard distribution:                     */
       /*   Gaussian with mean 2. and standard deviation 0.5.         */
       /* Choose a method: TDR with parameters                        */
       /*   c = 0: use T(x)=log(x) for the transformation;            */
       /*   variant "immediate acceptance";                           */
       /*   number of construction points = 10.                       */
       gen = unur_str2gen(
            "normal(2,0.5) & method=tdr; c=0.; variant_ia; cpoints=10");

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* Now you can use the generator object `gen' to sample from   */
       /* the distribution. Eg.:                                      */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* It is possible with method TDR to truncate the distribution */
       /* for an existing generator object ...                        */
       unur_tdr_chg_truncated( gen, -1., 0. );

       /* ... and sample again.                                       */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* When you do not need the generator object any more, you     */
       /* can destroy it.                                             */
       unur_free(gen);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

2.9 Change uniform random generator
===================================

All generator object use the same default uniform random number generator by
default. This can be changed to any generator of your choice such that each
generator object has its own random number generator or can share it with some
other objects.  It is also possible to change the default generator at any time.
See *note Using uniform random number generators: URNG, for details.

   The following example shows how the uniform random number generator can be
set or changed for a generator object. It requires the RNGSTREAMS library to be
installed and used. Otherwise the example must be modified accordingly.

     /* ------------------------------------------------------------- */
     /* File: example_rngstreams.c                                    */
     /* ------------------------------------------------------------- */
     #ifdef UNURAN_SUPPORTS_RNGSTREAM
     /* ------------------------------------------------------------- */
     /* This example makes use of the RNGSTREAM library for           */
     /* for generating uniform random numbers.                        */
     /* (see http://statmath.wu.ac.at/software/RngStreams/)           */
     /* To compile this example you must have set                     */
     /*   ./configure --with-urng-rngstream                           */
     /* (Of course the executable has to be linked against the        */
     /* RNGSTREAM library.)                                           */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header files.                                  */
     #include <unuran.h>
     #include <unuran_urng_rngstreams.h>

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;          /* loop variable                            */
       double x;          /* will hold the random number              */
       double fparams[2]; /* array for parameters for distribution    */

       /* Declare the three UNURAN objects.                           */
       UNUR_DISTR *distr;    /* distribution object                   */
       UNUR_PAR   *par;      /* parameter object                      */
       UNUR_GEN   *gen;      /* generator object                      */

       /* Declare objects for uniform random number generators.       */
       UNUR_URNG  *urng1, *urng2;    /* uniform generator objects     */

       /* The RNGSTREAMS library sets a package seed.                 */
       unsigned long seed[] = {111u, 222u, 333u, 444u, 555u, 666u};
       RngStream_SetPackageSeed(seed);

       /* RngStreams only:                                            */
       /* Make a object for uniform random number generator.          */
       /* For details see                                             */
       /* http://statmath.wu.ac.at/software/RngStreams/               */
       urng1 = unur_urng_rngstream_new("urng-1");
       if (urng1 == NULL) exit (EXIT_FAILURE);

       /* Use a predefined standard distribution:                     */
       /*   Beta with parameters 2 and 3.                             */
       fparams[0] = 2.;
       fparams[1] = 3.;
       distr = unur_distr_beta( fparams, 2 );

       /* Choose a method: TDR.                                       */
       par = unur_tdr_new(distr);

       /* Set uniform generator in parameter object                   */
       unur_set_urng( par, urng1 );

       /* Create the generator object.                                */
       gen = unur_init(par);

       /* Notice that this call has also destroyed the parameter      */
       /* object `par' as a side effect.                              */

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* It is possible to reuse the distribution object to create   */
       /* another generator object. If you do not need it any more,   */
       /* it should be destroyed to free memory.                      */
       unur_distr_free(distr);

       /* Now you can use the generator object `gen' to sample from   */
       /* the distribution. Eg.:                                      */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* Now we want to switch to a different (independent) stream   */
       /* of uniform random numbers.                                  */
       urng2 = unur_urng_rngstream_new("urng-2");
       if (urng2 == NULL) exit (EXIT_FAILURE);
       unur_chg_urng( gen, urng2 );

       /* ... and sample again.                                       */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* When you do not need the generator object any more, you     */
       /* can destroy it.                                             */
       unur_free(gen);

       /* We also should destroy the uniform random number generators.*/
       unur_urng_free(urng1);
       unur_urng_free(urng2);

       exit (EXIT_SUCCESS);
     } /* end of main() */

     /* ------------------------------------------------------------- */
     #else
     #include <stdio.h>
     #include <stdlib.h>
     int main(void) {
       printf("You must enable the RNGSTREAM library to run this example!\n\n");
       exit (77);    /* exit code for automake check routines */
     }
     #endif
     /* ------------------------------------------------------------- */

2.10 Sample pairs of antithetic random variates
===============================================

Using Method TDR it is easy to sample pairs of antithetic random variates.

     /* ------------------------------------------------------------- */
     /* File: example_anti.c                                          */
     /* ------------------------------------------------------------- */
     #ifdef UNURAN_SUPPORTS_PRNG
     /* ------------------------------------------------------------- */
     /* This example makes use of the PRNG library for generating     */
     /* uniform random numbers.                                       */
     /* (see http://statmath.wu.ac.at/prng/)                          */
     /* To compile this example you must have set                     */
     /*   ./configure --with-urng-prng                                */
     /* (Of course the executable has to be linked against the        */
     /* PRNG library.)                                                */
     /* ------------------------------------------------------------- */

     /* Example how to sample from two streams of antithetic random   */
     /* variates from Gaussian N(2,5) and Gamma(4) distribution, resp.*/

     /* ------------------------------------------------------------- */

     /* Include UNURAN header files.                                  */
     #include <unuran.h>
     #include <unuran_urng_prng.h>

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;          /* loop variable                            */
       double xn, xg;     /* will hold the random number              */
       double fparams[2]; /* array for parameters for distribution    */

       /* Declare the three UNURAN objects.                           */
       UNUR_DISTR *distr;    /* distribution object                   */
       UNUR_PAR   *par;      /* parameter object                      */
       UNUR_GEN   *gen_normal, *gen_gamma;
                             /* generator objects                     */

       /* Declare objects for uniform random number generators.       */
       UNUR_URNG  *urng1, *urng2;    /* uniform generator objects     */

       /* PRNG only:                                                  */
       /* Make a object for uniform random number generator.          */
       /* For details see http://statmath.wu.ac.at/prng/.             */


       /* The first generator: Gaussian N(2,5) */

       /* uniform generator: We use the Mersenne Twister.             */
       urng1 = unur_urng_prng_new("mt19937(1237)");
       if (urng1 == NULL) exit (EXIT_FAILURE);

       /* UNURAN generator object for N(2,5) */
       fparams[0] = 2.;
       fparams[1] = 5.;
       distr = unur_distr_normal( fparams, 2 );

       /* Choose method TDR with variant PS.                          */
       par = unur_tdr_new( distr );
       unur_tdr_set_variant_ps( par );

       /* Set uniform generator in parameter object.                  */
       unur_set_urng( par, urng1 );

       /* Set auxilliary uniform random number generator.             */
       /* We use the default generator.                               */
       unur_use_urng_aux_default( par );

       /* Alternatively you can create and use your own auxilliary    */
       /* uniform random number generator:                            */
       /*    UNUR_URNG  *urng_aux;                                    */
       /*    urng_aux = unur_urng_prng_new("tt800");                  */
       /*    if (urng_aux == NULL) exit (EXIT_FAILURE);               */
       /*    unur_set_urng_aux( par, urng_aux );                      */

       /* Create the generator object.                                */
       gen_normal = unur_init(par);
       if (gen_normal == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* Destroy distribution object (gen_normal has its own copy).  */
       unur_distr_free(distr);


       /* The second generator: Gamma(4) with antithetic variates.    */

       /* uniform generator: We use the Mersenne Twister.             */
       urng2 = unur_urng_prng_new("anti(mt19937(1237))");
       if (urng2 == NULL) exit (EXIT_FAILURE);

       /* UNURAN generator object for gamma(4) */
       fparams[0] = 4.;
       distr = unur_distr_gamma( fparams, 1 );

       /* Choose method TDR with variant PS.                          */
       par = unur_tdr_new( distr );
       unur_tdr_set_variant_ps( par );

       /* Set uniform generator in parameter object.                  */
       unur_set_urng( par, urng2 );

       /* Set auxilliary uniform random number generator.             */
       /* We use the default generator.                               */
       unur_use_urng_aux_default( par );

       /* Alternatively you can create and use your own auxilliary    */
       /* uniform random number generator (see above).                */
       /* Notice that both generator objects gen_normal and           */
       /* gen_gamma can share the same auxilliary URNG.               */

       /* Create the generator object.                                */
       gen_gamma = unur_init(par);
       if (gen_gamma == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* Destroy distribution object (gen_normal has its own copy).  */
       unur_distr_free(distr);


       /* Now we can sample pairs of negatively correlated random     */
       /* variates. E.g.:                                             */
       for (i=0; i<10; i++) {
         xn = unur_sample_cont(gen_normal);
         xg = unur_sample_cont(gen_gamma);
         printf("%g, %g\n",xn,xg);
       }


       /* When you do not need the generator objects any more, you    */
       /* can destroy it.                                             */
       unur_free(gen_normal);
       unur_free(gen_gamma);

       /* We also should destroy the uniform random number generators.*/
       unur_urng_free(urng1);
       unur_urng_free(urng2);

       exit (EXIT_SUCCESS);
     } /* end of main() */

     /* ------------------------------------------------------------- */
     #else
     #include <stdio.h>
     #include <stdlib.h>
     int main(void) {
       printf("You must enable the PRNG library to run this example!\n\n");
       exit (77);    /* exit code for automake check routines */
     }
     #endif
     /* ------------------------------------------------------------- */

2.11 Sample pairs of antithetic random variates (String API)
============================================================

Using Method TDR it is easy to sample pairs of antithetic random variates.

     /* ------------------------------------------------------------- */
     /* File: example_anti_str.c                                      */
     /* ------------------------------------------------------------- */
     /* String API.                                                   */
     /* ------------------------------------------------------------- */
     #ifdef UNURAN_SUPPORTS_PRNG
     /* ------------------------------------------------------------- */
     /* This example makes use of the PRNG library for generating     */
     /* uniform random numbers.                                       */
     /* (see http://statmath.wu.ac.at/prng/)                          */
     /* To compile this example you must have set                     */
     /*   ./configure --with-urng-prng                                */
     /* (Of course the executable has to be linked against the        */
     /* PRNG library.)                                                */
     /* ------------------------------------------------------------- */

     /* Example how to sample from two streams of antithetic random   */
     /* variates from Gaussian N(2,5) and Gamma(4) distribution, resp.*/

     /* ------------------------------------------------------------- */

     /* Include UNURAN header files.                                  */
     #include <unuran.h>
     #include <unuran_urng_prng.h>

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;          /* loop variable                            */
       double xn, xg;     /* will hold the random number              */

       /* Declare UNURAN generator objects.                           */
       UNUR_GEN   *gen_normal, *gen_gamma;

       /* PRNG only:                                                  */
       /* Make a object for uniform random number generator.          */
       /* For details see http://statmath.wu.ac.at/prng/.             */

       /* Create the first generator: Gaussian N(2,5)                 */
       gen_normal = unur_str2gen("normal(2,5) & method=tdr; variant_ps & urng=mt19937(1237)");
       if (gen_normal == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }
       /* Set auxilliary uniform random number generator.             */
       /* We use the default generator.                               */
       unur_chgto_urng_aux_default(gen_normal);

       /* The second generator: Gamma(4) with antithetic variates.    */
       gen_gamma = unur_str2gen("gamma(4) & method=tdr; variant_ps & urng=anti(mt19937(1237))");
       if (gen_gamma == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }
       unur_chgto_urng_aux_default(gen_gamma);


       /* Now we can sample pairs of negatively correlated random     */
       /* variates. E.g.:                                             */
       for (i=0; i<10; i++) {
         xn = unur_sample_cont(gen_normal);
         xg = unur_sample_cont(gen_gamma);
         printf("%g, %g\n",xn,xg);
       }


       /* When you do not need the generator objects any more, you    */
       /* can destroy it.                                             */

       /* But first we have to destroy the uniform random number      */
       /* generators.                                                 */
       unur_urng_free(unur_get_urng(gen_normal));
       unur_urng_free(unur_get_urng(gen_gamma));

       unur_free(gen_normal);
       unur_free(gen_gamma);


       exit (EXIT_SUCCESS);
     } /* end of main() */

     /* ------------------------------------------------------------- */
     #else
     #include <stdio.h>
     #include <stdlib.h>
     int main(void) {
       printf("You must enable the PRNG library to run this example!\n\n");
       exit (77);    /* exit code for automake check routines */
     }
     #endif
     /* ------------------------------------------------------------- */

2.12 More examples
==================

*Note Methods for continuous univariate distributions: Methods_for_CONT.

   *Note Methods for continuous empirical univariate distributions:
Methods_for_CEMP.

   *Note Methods for continuous empirical multivariate distributions:
Methods_for_CVEMP.

   *Note Methods for discrete univariate distributions: Methods_for_DISCR.

3 String Interface
******************

The string interface (string API) provided by the `unur_str2gen' call is the
easiest way to use UNU.RAN. This function takes a character string as its
argument. The string is parsed and the information obtained is used to create a
generator object. It returns `NULL' if this fails, either due to a syntax
error, or due to invalid data. In both cases `unur_error' is set to the
corresponding error codes (*note Error reporting: Error_reporting.).
Additionally there exists the call `unur_str2distr' that only produces a
distribution object.

   Notice that the string interface does not implement all features of the
UNU.RAN library. For trickier tasks it might be necessary to use the UNU.RAN
calls.

   In *note Examples::, all examples are given using both the UNU.RAN standard
API and this convenient string API.  The corresponding programm codes are
equivalent.

Function reference
------------------

 -- Function: UNUR_GEN* unur_str2gen (const CHAR* STRING)
     Get a generator object for the distribution, method and uniform random
     number generator as described in the given STRING.  See *note Syntax of
     String Interface: StringSyntax, for details.

 -- Function: UNUR_DISTR* unur_str2distr (const CHAR* STRING)
     Get a distribution object for the distribution described in STRING.  See
     *note Syntax of String Interface: StringSyntax, and *note Distribution
     String: StringDistr, for details. However, only the block for the
     distribution object is allowed.

 -- Function: UNUR_GEN* unur_makegen_ssu (const CHAR* DISTRSTR, const CHAR*
          METHODSTR, UNUR_URNG* URNG)
 -- Function: UNUR_GEN* unur_makegen_dsu (const UNUR_DISTR* DISTRIBUTION, const
          CHAR* METHODSTR, UNUR_URNG* URNG)
     Make a generator object for the distribution, method and uniform random
     number generator. The distribution can be given either as string DISTRSTR
     or as a distribution object DISTR.  The method must be given as a string
     METHODSTR.  For the syntax of these strings see *note Syntax of String
     Interface: StringSyntax.  However, the `method' keyword is optional for
     these calls and can be omitted. If METHODSTR is the empty (blank) string
     or `NULL' method AUTO is used.  The uniform random number generator is
     optional. If URNG is `NULL' then the default uniform random number
     generator is used.

3.1 Syntax of String Interface
==============================

The given string holds information about the requested distribution and
(optional) about the sampling method and the uniform random number generator
invoked. The interpretation of the string is not case-sensitive, all white
spaces are ignored.

   The string consists of up to three blocks, separated by ampersands `&'.

   Each block consists of `<key>=<value>' pairs, separated by semicolons `;'.

   The first key in each block is used to indicate each block.  We have three
different blocks with the following (first) keys:
`distr'
     definition of the distribution (*note Distribution String: StringDistr.).

`method'
     description of the transformation method (*note Method String:
     StringMethod.).

`urng'
     uniform random number generation (*note Uniform RNG String: StringURNG.).

   The `distr' block must be the very first block and is obligatory. All the
other blocks are optional and can be arranged in arbitrary order.

   For details see the following description of each block.

   In the following example
     distr = normal(3.,0.75); domain = (0,inf) & method = tdr; c = 0
   we have a distribution block for the truncated normal distribution with mean
3 and standard deviation 0.75 on domain (0,infinity); and block for choosing
method TDR with parameter c set to 0.


   The `<key>=<value>' pairs that follow the first (initial) pair in each block
are used to set parameters.  The name of the parameter is given by the `<key>'
string. It is deduced from the UNU.RAN set calls by taking the part after
`..._set_'.  The `<value>' string holds the parameters to be set, separated by
commata `,'.  There are three types of parameters:
_string `"..."'_
     i.e. any sequence of characters enclosed by double quotes `"..."',

_list `(...,...)'_
     i.e. list of _numbers_, separated by commata `,', enclosed in parenthesis
     `(...)', and

_number_
     a sequence of characters that is not enclosed by quotes `"..."' or
     parenthesis `(...)'.  It is interpreted as float or integer depending on
     the type of the corresponding parameter.
   The `<value>' string (including the character `=') can be omitted when no
argument is required.

   At the moment not all `set' calls are supported.  The syntax for the
`<value>' can be directly derived from the corresponding `set' calls. To
simplify the syntax additional shortcuts are possible. The following table
lists the parameters for the `set' calls that are supported by the string
interface; the entry in parenthesis gives the type of the argument as `<value>'
string:

`int  (number):'
     The number is interpreted as an integer.  `true' and `on' are transformed
     to `1', `false' and `off' are transformed to `0'.  A missing argument is
     interpreted as `1'.

`int, int  (number, number or list):'
     The two numbers or the first two entries in the list are interpreted as a
     integers.  `inf' and `-inf' are transformed to `INT_MAX' and `INT_MIN'
     respectively, i.e. the largest and smallest integers that can be
     represented by the computer.

`unsigned (number):'
     The number is interpreted as an unsigned hexadecimal integer.

`double  (number):'
     The number is interpreted as a floating point number.  `inf' is
     transformed to `UNUR_INFINITY'.

`double, double  (number, number or list):'
     The two numbers or the first two entries in the list are interpreted as a
     floating point numbers.  `inf' is transformed to `UNUR_INFINITY'. However
     using `inf' in the list might not work for all versions of C. Then it is
     recommended to use two single numbers instead of a list.

`int, double*  ([number,] list or number):'
        - The list is interpreted as a double array.  The (first) number as its
          length.  If it is less than the actual size of the array only the
          first entries of the array are used.

        - If only the list is given (i.e., if the first number is omitted), the
          first number is set to the actual size of the array.

        - If only the number is given (i.e., if the list is omitted), the `NULL'
          pointer is used instead an array as argument.

`double*, int  (list [,number]):'
     The list is interpreted as a double array.  The (second) number as its
     length.  If the length is omitted, it is replaced by the actual size of the
     array. (Only in the `distribution' block!)

`char*  (string):'
     The character string is passed as is to the corresponding set call.


   Notice that missing entries in a list of numbers are interpreted as `0'.
E.g, a the list `(1,,3)' is read as `(1,0,3)', the list `(1,2,)' as `(1,2,0)'.

   The the list of `key' strings in *note Keys for Distribution String:
KeysDistr, and *note Keys for Method String: KeysMethod, for further details.

3.2 Distribution String
=======================

The `distr' block must be the very first block and is obligatory. For that
reason the keyword `distr' is optional and can be omitted (together with the
`=' character).  Moreover it is ignored while parsing the string. However, to
avoid some possible confusion it has to start with the letter `d' (if it is
given at all).

   The value of the `distr' key is used to get the distribution object, either
via a `unur_distr_<value>' call for a standard distribution via a
`unur_distr_<value>_new' call to get an object of a generic distribution.
However not all generic distributions are supported yet.

   The parameters for the standard distribution are given as a list. There must
not be any character (other than white space) between the name of the standard
distribution and the opening parenthesis `(' of this list. E.g., to get a beta
distribution, use
     distr = beta(2,4)

   To get an object for a discrete distribution with probability vector
(0.5,0.2,0.3), use
     distr = discr; pv = (0.5,0.2,0.3)

   It is also possible to set a PDF, PMF, or CDF using a string.  E.g., to
create a continuous distribution with PDF proportional to `exp(-sqrt(2+(x-1)^2)
+ (x-1))' and domain (0,inf) use
     distr = cont; pdf = "exp(-sqrt(2+(x-1)^2) + (x-1))"
   Notice: If this string is used in an `unur_str2distr' or `unur_str2gen' call
the double quotes `"' must be protected by `\"'. Alternatively, single quotes
may be used instead
     distr = cont; pdf = 'exp(-sqrt(2+(x-1)^2) + (x-1))'

   For the details of function strings see *note Function String: StringFunct.

3.2.1 Keys for Distribution String
----------------------------------

List of standard distributions *note Standard distributions: Stddist.

   - `[distr =] beta(...)'      => *note beta::

   - `[distr =] binomial(...)'      => *note binomial::

   - `[distr =] cauchy(...)'      => *note cauchy::

   - `[distr =] chi(...)'      => *note chi::

   - `[distr =] chisquare(...)'      => *note chisquare::

   - `[distr =] exponential(...)'      => *note exponential::

   - `[distr =] extremeI(...)'      => *note extremeI::

   - `[distr =] extremeII(...)'      => *note extremeII::

   - `[distr =] F(...)'      => *note F::

   - `[distr =] gamma(...)'      => *note gamma::

   - `[distr =] geometric(...)'      => *note geometric::

   - `[distr =] gig(...)'      => *note gig::

   - `[distr =] gig2(...)'      => *note gig2::

   - `[distr =] hyperbolic(...)'      => *note hyperbolic::

   - `[distr =] hypergeometric(...)'      => *note hypergeometric::

   - `[distr =] ig(...)'      => *note ig::

   - `[distr =] laplace(...)'      => *note laplace::

   - `[distr =] logarithmic(...)'      => *note logarithmic::

   - `[distr =] logistic(...)'      => *note logistic::

   - `[distr =] lognormal(...)'      => *note lognormal::

   - `[distr =] lomax(...)'      => *note lomax::

   - `[distr =] negativebinomial(...)'      => *note negativebinomial::

   - `[distr =] normal(...)'      => *note normal::

   - `[distr =] pareto(...)'      => *note pareto::

   - `[distr =] poisson(...)'      => *note poisson::

   - `[distr =] powerexponential(...)'      => *note powerexponential::

   - `[distr =] rayleigh(...)'      => *note rayleigh::

   - `[distr =] slash(...)'      => *note slash::

   - `[distr =] student(...)'      => *note student::

   - `[distr =] triangular(...)'      => *note triangular::

   - `[distr =] uniform(...)'      => *note uniform::

   - `[distr =] weibull(...)'      => *note weibull::


   List of generic distributions *note Handling Distribution Objects:
Distribution_objects.

   - `[distr =] cemp'      => *note CEMP::

   - `[distr =] cont'      => *note CONT::

   - `[distr =] discr'      => *note DISCR::


   _Notice_: Order statistics for continuous distributions (*note CORDER::) are
supported by using the key `orderstatistics' for distributions of type `CONT'.


   List of keys that are available via the String API.  For description see the
corresponding UNU.RAN set calls.

   * All distribution types
    `name = "<string>"'
          => *note `unur_distr_set_name': funct:unur_distr_set_name.


   * `cemp'  (Distribution Type)    (*note CEMP::)
    `data = (<list>) [, <int>]'
          => *note `unur_distr_cemp_set_data': funct:unur_distr_cemp_set_data.

    `hist_bins = (<list>) [, <int>]'
          => *note `unur_distr_cemp_set_hist_bins':
          funct:unur_distr_cemp_set_hist_bins.

    `hist_domain = <double>, <double> | (<list>)'
          => *note `unur_distr_cemp_set_hist_domain':
          funct:unur_distr_cemp_set_hist_domain.

    `hist_prob = (<list>) [, <int>]'
          => *note `unur_distr_cemp_set_hist_prob':
          funct:unur_distr_cemp_set_hist_prob.


   * `cont'  (Distribution Type)    (*note CONT::)
    `cdf = "<string>"'
          => *note `unur_distr_cont_set_cdfstr':
          funct:unur_distr_cont_set_cdfstr.

    `center = <double>'
          => *note `unur_distr_cont_set_center':
          funct:unur_distr_cont_set_center.

    `domain = <double>, <double> | (<list>)'
          => *note `unur_distr_cont_set_domain':
          funct:unur_distr_cont_set_domain.

    `hr = "<string>"'
          => *note `unur_distr_cont_set_hrstr': funct:unur_distr_cont_set_hrstr.

    `logcdf = "<string>"'
          => *note `unur_distr_cont_set_logcdfstr':
          funct:unur_distr_cont_set_logcdfstr.

    `logpdf = "<string>"'
          => *note `unur_distr_cont_set_logpdfstr':
          funct:unur_distr_cont_set_logpdfstr.

    `mode = <double>'
          => *note `unur_distr_cont_set_mode': funct:unur_distr_cont_set_mode.

    `pdf = "<string>"'
          => *note `unur_distr_cont_set_pdfstr':
          funct:unur_distr_cont_set_pdfstr.

    `pdfarea = <double>'
          => *note `unur_distr_cont_set_pdfarea':
          funct:unur_distr_cont_set_pdfarea.

    `pdfparams = (<list>) [, <int>]'
          => *note `unur_distr_cont_set_pdfparams':
          funct:unur_distr_cont_set_pdfparams.

    `orderstatistics = <int>, <int> | (<list>)'
          Make order statistics for given distribution. The first parameter
          gives the sample size, the second parameter its rank.  (see *note
          `unur_distr_corder_new': funct:unur_distr_corder_new.)


   * `discr'  (Distribution Type)    (*note DISCR::)
    `cdf = "<string>"'
          => *note `unur_distr_discr_set_cdfstr':
          funct:unur_distr_discr_set_cdfstr.

    `domain = <int>, <int> | (<list>)'
          => *note `unur_distr_discr_set_domain':
          funct:unur_distr_discr_set_domain.

    `mode [= <int>]'
          => *note `unur_distr_discr_set_mode': funct:unur_distr_discr_set_mode.

    `pmf = "<string>"'
          => *note `unur_distr_discr_set_pmfstr':
          funct:unur_distr_discr_set_pmfstr.

    `pmfparams = (<list>) [, <int>]'
          => *note `unur_distr_discr_set_pmfparams':
          funct:unur_distr_discr_set_pmfparams.

    `pmfsum = <double>'
          => *note `unur_distr_discr_set_pmfsum':
          funct:unur_distr_discr_set_pmfsum.

    `pv = (<list>) [, <int>]'
          => *note `unur_distr_discr_set_pv': funct:unur_distr_discr_set_pv.


3.3 Function String
===================

In unuran it is also possible to define functions (e.g. CDF or PDF) as strings.
As you can see in Example 2 (*note Example_2_str::) it is very easy to define
the PDF of a distribution object by means of a string.  The possibilities using
this string interface are more restricted than using a pointer to a routine
coded in C (*note Example_2::).  But the differences in evaluation time is
small.  When a distribution object is defined using this string interface then
of course the same conditions on the given density or CDF must be satisfied for
a chosen method as for the standard API.  This string interface can be used for
both within the UNU.RAN string API using the `unur_str2gen' call, and for calls
that define the density or CDF for a particular distribution object as done with
(e.g.) the call `unur_distr_cont_set_pdfstr'.  Here is an example for the
latter case:

     unur_distr_cont_set_pdfstr(distr,"1-x*x");

Syntax
------

The syntax for the function string is case insensitive, white spaces are
ingnored. The expressions are similar to most programming languages and
mathematical programs (see also the examples below). It is especially
influenced by C. The usual preceedence rules are used (from highest to lowest
preceedence: functions, power, multiplication, addition, relation operators).
Use parentheses in case of doubt or when these preceedences should be changed.

   Relation operators can be used as indicator functions, i.e. the term `(x>1)'
is evaluted as `1' if this relation is satisfied, and as `0' otherwise.

   The first unknown symbol (letter or word) is interpreted as the variable of
the function. It is recommended to use `x'.  Only one variable can be used.


_Important_: The symbol `e' is used twice, for Euler's constant (= 2.7182...)
and as exponent.  The multiplication operator `*' must not be omitted, i.e.  `2
x' is interpreted as the string `2x' (which will result in a syntax error).

List of symbols
---------------

Numbers

Numbers are composed using digits and, optionally, a sign, a decimal point, and
an exponent indicated by `e'.

Symbol    Explanation                 Examples
`0...9'   digits                      `2343'
`.'       decimal point               `165.567'
`-'       negative sign               `-465.223'
`e'       exponet                     `13.2e-4' (=0.00132)


Constants

`pi'      pi = 3.1415...              `3*pi+2'
`e'       Euler's constant            `3*e+2' (= 10.15...; do not
                                      cofuse with `3e2' = 300)
`inf'     infinity                    (used for domains)


Special symbols

`('       opening parenthesis         `2*(3+x)'
`)'       closing parenthesis         `2*(3+x)'
`,'       (argument) list separator   `mod(13,2)'


Relation operators (Indicator functions)

`<'       less than                   `(x<1)'
`='       equal                       `(2=x)'
`=='      same as `='                 `(x==3)'
`>'       greater than                `(x>0)'
`<='      less than or equal          `(x<=1)'
`!='      not equal                   `(x!0)'
`<>'      same as `!='                `(x<>pi)'
`>='      greater or equal            `(x>=1)'


Arithmetic operators

`+'       addition                    `2+x'
`-'       subtraction                 `2-x'
`*'       multiplication              `2*x'
`/'       division                    `x/2'
`^'       power                       `x^2'


Functions

`mod'     `mod(m,n)' remainder of     mod(x,2)
          devision m over n           
`exp'     exponential function (same  `exp(-x^2)' (same as `e^(-x^2)')
          as `e^x')                   
`log'     natural logarithm           `log(x)'
`sin'     sine                        `sin(x)'
`cos'     cosine                      `cos(x)'
`tan'     tangent                     `tan(x)'
`sec'     secant                      `sec(x*2)'
`sqrt'    square root                 `sqrt(2*x)'
`abs'     absolute value              `abs(x)'
`sgn'     sign function               `sign(x)*3'


Variable

`x'       variable                    `3*x^2'

Examples
--------


     1.231+7.9876*x-1.234e-3*x^2+3.335e-5*x^3

     sin(2*pi*x)+x^2

     exp(-((x-3)/2.1)^2)

It is also possible to define functions using different terms on separate
domains. However, instead of constructs using `if ... then ... else ...'
indicator functions are available.

For example to define the density of triangular distribution with domain (-1,1)
and mode 0 use

     (x>-1)*(x<0)*(1+x) + (x>=0)*(x<1)*(1-x)

3.4 Method String
=================

The key `method' is obligatory, it must be the first key and its value is the
name of a method suitable for the choosen standard distribution. E.g., if
method AROU is chosen, use
     method = arou

   Of course the all following keys dependend on the method choosen at first.
All corresponding `set' calls of UNU.RAN are available and the key is the
string after the `unur_<methodname>_set_' part of the command. E.g., UNU.RAN
provides the command `unur_arou_set_max_sqhratio' to set a parameter of method
AROU.  To call this function via the string-interface, the key `max_sqhratio'
can be used:
     max_sqhratio = 0.9
   Additionally the keyword `debug' can be used to set debugging flags (see
*note Debugging: Debug, for details).

   If this block is omitted, a suitable default method is used. Notice however
that the default method may change in future versions of UNU.RAN.

3.4.1 Keys for Method String
----------------------------

List of methods and keys that are available via the String API.  For
description see the corresponding UNU.RAN set calls.

   * `method = arou'      => `unur_arou_new' (*note AROU::)
    `cpoints = <int> [, (<list>)] | (<list>)'
          => *note `unur_arou_set_cpoints': funct:unur_arou_set_cpoints.

    `darsfactor = <double>'
          => *note `unur_arou_set_darsfactor': funct:unur_arou_set_darsfactor.

    `guidefactor = <double>'
          => *note `unur_arou_set_guidefactor': funct:unur_arou_set_guidefactor.

    `max_segments [= <int>]'
          => *note `unur_arou_set_max_segments':
          funct:unur_arou_set_max_segments.

    `max_sqhratio = <double>'
          => *note `unur_arou_set_max_sqhratio':
          funct:unur_arou_set_max_sqhratio.

    `pedantic [= <int>]'
          => *note `unur_arou_set_pedantic': funct:unur_arou_set_pedantic.

    `usecenter [= <int>]'
          => *note `unur_arou_set_usecenter': funct:unur_arou_set_usecenter.

    `usedars [= <int>]'
          => *note `unur_arou_set_usedars': funct:unur_arou_set_usedars.

    `verify [= <int>]'
          => *note `unur_arou_set_verify': funct:unur_arou_set_verify.


   * `method = ars'      => `unur_ars_new' (*note ARS::)
    `cpoints = <int> [, (<list>)] | (<list>)'
          => *note `unur_ars_set_cpoints': funct:unur_ars_set_cpoints.

    `max_intervals [= <int>]'
          => *note `unur_ars_set_max_intervals':
          funct:unur_ars_set_max_intervals.

    `max_iter [= <int>]'
          => *note `unur_ars_set_max_iter': funct:unur_ars_set_max_iter.

    `pedantic [= <int>]'
          => *note `unur_ars_set_pedantic': funct:unur_ars_set_pedantic.

    `reinit_ncpoints [= <int>]'
          => *note `unur_ars_set_reinit_ncpoints':
          funct:unur_ars_set_reinit_ncpoints.

    `reinit_percentiles = <int> [, (<list>)] | (<list>)'
          => *note `unur_ars_set_reinit_percentiles':
          funct:unur_ars_set_reinit_percentiles.

    `verify [= <int>]'
          => *note `unur_ars_set_verify': funct:unur_ars_set_verify.


   * `method = auto'      => `unur_auto_new' (*note AUTO::)
    `logss [= <int>]'
          => *note `unur_auto_set_logss': funct:unur_auto_set_logss.


   * `method = cstd'      => `unur_cstd_new' (*note CSTD::)
    `variant = <unsigned>'
          => *note `unur_cstd_set_variant': funct:unur_cstd_set_variant.


   * `method = dari'      => `unur_dari_new' (*note DARI::)
    `cpfactor = <double>'
          => *note `unur_dari_set_cpfactor': funct:unur_dari_set_cpfactor.

    `squeeze [= <int>]'
          => *note `unur_dari_set_squeeze': funct:unur_dari_set_squeeze.

    `tablesize [= <int>]'
          => *note `unur_dari_set_tablesize': funct:unur_dari_set_tablesize.

    `verify [= <int>]'
          => *note `unur_dari_set_verify': funct:unur_dari_set_verify.


   * `method = dau'      => `unur_dau_new' (*note DAU::)
    `urnfactor = <double>'
          => *note `unur_dau_set_urnfactor': funct:unur_dau_set_urnfactor.


   * `method = dgt'      => `unur_dgt_new' (*note DGT::)
    `guidefactor = <double>'
          => *note `unur_dgt_set_guidefactor': funct:unur_dgt_set_guidefactor.

    `variant = <unsigned>'
          => *note `unur_dgt_set_variant': funct:unur_dgt_set_variant.


   * `method = dsrou'      => `unur_dsrou_new' (*note DSROU::)
    `cdfatmode = <double>'
          => *note `unur_dsrou_set_cdfatmode': funct:unur_dsrou_set_cdfatmode.

    `verify [= <int>]'
          => *note `unur_dsrou_set_verify': funct:unur_dsrou_set_verify.


   * `method = dstd'      => `unur_dstd_new' (*note DSTD::)
    `variant = <unsigned>'
          => *note `unur_dstd_set_variant': funct:unur_dstd_set_variant.


   * `method = empk'      => `unur_empk_new' (*note EMPK::)
    `beta = <double>'
          => *note `unur_empk_set_beta': funct:unur_empk_set_beta.

    `kernel = <unsigned>'
          => *note `unur_empk_set_kernel': funct:unur_empk_set_kernel.

    `positive [= <int>]'
          => *note `unur_empk_set_positive': funct:unur_empk_set_positive.

    `smoothing = <double>'
          => *note `unur_empk_set_smoothing': funct:unur_empk_set_smoothing.

    `varcor [= <int>]'
          => *note `unur_empk_set_varcor': funct:unur_empk_set_varcor.


   * `method = gibbs'      => `unur_gibbs_new' (*note GIBBS::)
    `burnin [= <int>]'
          => *note `unur_gibbs_set_burnin': funct:unur_gibbs_set_burnin.

    `c = <double>'
          => *note `unur_gibbs_set_c': funct:unur_gibbs_set_c.

    `thinning [= <int>]'
          => *note `unur_gibbs_set_thinning': funct:unur_gibbs_set_thinning.

    `variant_coordinate'
          => *note `unur_gibbs_set_variant_coordinate':
          funct:unur_gibbs_set_variant_coordinate.

    `variant_random_direction'
          => *note `unur_gibbs_set_variant_random_direction':
          funct:unur_gibbs_set_variant_random_direction.


   * `method = hinv'      => `unur_hinv_new' (*note HINV::)
    `boundary = <double>, <double> | (<list>)'
          => *note `unur_hinv_set_boundary': funct:unur_hinv_set_boundary.

    `cpoints = (<list>), <int>'
          => *note `unur_hinv_set_cpoints': funct:unur_hinv_set_cpoints.

    `guidefactor = <double>'
          => *note `unur_hinv_set_guidefactor': funct:unur_hinv_set_guidefactor.

    `max_intervals [= <int>]'
          => *note `unur_hinv_set_max_intervals':
          funct:unur_hinv_set_max_intervals.

    `order [= <int>]'
          => *note `unur_hinv_set_order': funct:unur_hinv_set_order.

    `u_resolution = <double>'
          => *note `unur_hinv_set_u_resolution':
          funct:unur_hinv_set_u_resolution.


   * `method = hitro'      => `unur_hitro_new' (*note HITRO::)
    `adaptive_multiplier = <double>'
          => *note `unur_hitro_set_adaptive_multiplier':
          funct:unur_hitro_set_adaptive_multiplier.

    `burnin [= <int>]'
          => *note `unur_hitro_set_burnin': funct:unur_hitro_set_burnin.

    `r = <double>'
          => *note `unur_hitro_set_r': funct:unur_hitro_set_r.

    `thinning [= <int>]'
          => *note `unur_hitro_set_thinning': funct:unur_hitro_set_thinning.

    `use_adaptiveline [= <int>]'
          => *note `unur_hitro_set_use_adaptiveline':
          funct:unur_hitro_set_use_adaptiveline.

    `use_adaptiverectangle [= <int>]'
          => *note `unur_hitro_set_use_adaptiverectangle':
          funct:unur_hitro_set_use_adaptiverectangle.

    `use_boundingrectangle [= <int>]'
          => *note `unur_hitro_set_use_boundingrectangle':
          funct:unur_hitro_set_use_boundingrectangle.

    `v = <double>'
          => *note `unur_hitro_set_v': funct:unur_hitro_set_v.

    `variant_coordinate'
          => *note `unur_hitro_set_variant_coordinate':
          funct:unur_hitro_set_variant_coordinate.

    `variant_random_direction'
          => *note `unur_hitro_set_variant_random_direction':
          funct:unur_hitro_set_variant_random_direction.


   * `method = hrb'      => `unur_hrb_new' (*note HRB::)
    `upperbound = <double>'
          => *note `unur_hrb_set_upperbound': funct:unur_hrb_set_upperbound.

    `verify [= <int>]'
          => *note `unur_hrb_set_verify': funct:unur_hrb_set_verify.


   * `method = hrd'      => `unur_hrd_new' (*note HRD::)
    `verify [= <int>]'
          => *note `unur_hrd_set_verify': funct:unur_hrd_set_verify.


   * `method = hri'      => `unur_hri_new' (*note HRI::)
    `p0 = <double>'
          => *note `unur_hri_set_p0': funct:unur_hri_set_p0.

    `verify [= <int>]'
          => *note `unur_hri_set_verify': funct:unur_hri_set_verify.


   * `method = itdr'      => `unur_itdr_new' (*note ITDR::)
    `cp = <double>'
          => *note `unur_itdr_set_cp': funct:unur_itdr_set_cp.

    `ct = <double>'
          => *note `unur_itdr_set_ct': funct:unur_itdr_set_ct.

    `verify [= <int>]'
          => *note `unur_itdr_set_verify': funct:unur_itdr_set_verify.

    `xi = <double>'
          => *note `unur_itdr_set_xi': funct:unur_itdr_set_xi.


   * `method = mvtdr'      => `unur_mvtdr_new' (*note MVTDR::)
    `boundsplitting = <double>'
          => *note `unur_mvtdr_set_boundsplitting':
          funct:unur_mvtdr_set_boundsplitting.

    `maxcones [= <int>]'
          => *note `unur_mvtdr_set_maxcones': funct:unur_mvtdr_set_maxcones.

    `stepsmin [= <int>]'
          => *note `unur_mvtdr_set_stepsmin': funct:unur_mvtdr_set_stepsmin.

    `verify [= <int>]'
          => *note `unur_mvtdr_set_verify': funct:unur_mvtdr_set_verify.


   * `method = ninv'      => `unur_ninv_new' (*note NINV::)
    `max_iter [= <int>]'
          => *note `unur_ninv_set_max_iter': funct:unur_ninv_set_max_iter.

    `start = <double>, <double> | (<list>)'
          => *note `unur_ninv_set_start': funct:unur_ninv_set_start.

    `table [= <int>]'
          => *note `unur_ninv_set_table': funct:unur_ninv_set_table.

    `u_resolution = <double>'
          => *note `unur_ninv_set_u_resolution':
          funct:unur_ninv_set_u_resolution.

    `usebisect'
          => *note `unur_ninv_set_usebisect': funct:unur_ninv_set_usebisect.

    `usenewton'
          => *note `unur_ninv_set_usenewton': funct:unur_ninv_set_usenewton.

    `useregula'
          => *note `unur_ninv_set_useregula': funct:unur_ninv_set_useregula.

    `x_resolution = <double>'
          => *note `unur_ninv_set_x_resolution':
          funct:unur_ninv_set_x_resolution.


   * `method = nrou'      => `unur_nrou_new' (*note NROU::)
    `center = <double>'
          => *note `unur_nrou_set_center': funct:unur_nrou_set_center.

    `r = <double>'
          => *note `unur_nrou_set_r': funct:unur_nrou_set_r.

    `u = <double>, <double> | (<list>)'
          => *note `unur_nrou_set_u': funct:unur_nrou_set_u.

    `v = <double>'
          => *note `unur_nrou_set_v': funct:unur_nrou_set_v.

    `verify [= <int>]'
          => *note `unur_nrou_set_verify': funct:unur_nrou_set_verify.


   * `method = pinv'      => `unur_pinv_new' (*note PINV::)
    `boundary = <double>, <double> | (<list>)'
          => *note `unur_pinv_set_boundary': funct:unur_pinv_set_boundary.

    `keepcdf [= <int>]'
          => *note `unur_pinv_set_keepcdf': funct:unur_pinv_set_keepcdf.

    `max_intervals [= <int>]'
          => *note `unur_pinv_set_max_intervals':
          funct:unur_pinv_set_max_intervals.

    `order [= <int>]'
          => *note `unur_pinv_set_order': funct:unur_pinv_set_order.

    `searchboundary = <int>, <int> | (<list>)'
          => *note `unur_pinv_set_searchboundary':
          funct:unur_pinv_set_searchboundary.

    `smoothness [= <int>]'
          => *note `unur_pinv_set_smoothness': funct:unur_pinv_set_smoothness.

    `u_resolution = <double>'
          => *note `unur_pinv_set_u_resolution':
          funct:unur_pinv_set_u_resolution.

    `use_upoints [= <int>]'
          => *note `unur_pinv_set_use_upoints': funct:unur_pinv_set_use_upoints.

    `usecdf'
          => *note `unur_pinv_set_usecdf': funct:unur_pinv_set_usecdf.

    `usepdf'
          => *note `unur_pinv_set_usepdf': funct:unur_pinv_set_usepdf.


   * `method = srou'      => `unur_srou_new' (*note SROU::)
    `cdfatmode = <double>'
          => *note `unur_srou_set_cdfatmode': funct:unur_srou_set_cdfatmode.

    `pdfatmode = <double>'
          => *note `unur_srou_set_pdfatmode': funct:unur_srou_set_pdfatmode.

    `r = <double>'
          => *note `unur_srou_set_r': funct:unur_srou_set_r.

    `usemirror [= <int>]'
          => *note `unur_srou_set_usemirror': funct:unur_srou_set_usemirror.

    `usesqueeze [= <int>]'
          => *note `unur_srou_set_usesqueeze': funct:unur_srou_set_usesqueeze.

    `verify [= <int>]'
          => *note `unur_srou_set_verify': funct:unur_srou_set_verify.


   * `method = ssr'      => `unur_ssr_new' (*note SSR::)
    `cdfatmode = <double>'
          => *note `unur_ssr_set_cdfatmode': funct:unur_ssr_set_cdfatmode.

    `pdfatmode = <double>'
          => *note `unur_ssr_set_pdfatmode': funct:unur_ssr_set_pdfatmode.

    `usesqueeze [= <int>]'
          => *note `unur_ssr_set_usesqueeze': funct:unur_ssr_set_usesqueeze.

    `verify [= <int>]'
          => *note `unur_ssr_set_verify': funct:unur_ssr_set_verify.


   * `method = tabl'      => `unur_tabl_new' (*note TABL::)
    `areafraction = <double>'
          => *note `unur_tabl_set_areafraction':
          funct:unur_tabl_set_areafraction.

    `boundary = <double>, <double> | (<list>)'
          => *note `unur_tabl_set_boundary': funct:unur_tabl_set_boundary.

    `cpoints = <int> [, (<list>)] | (<list>)'
          => *note `unur_tabl_set_cpoints': funct:unur_tabl_set_cpoints.

    `darsfactor = <double>'
          => *note `unur_tabl_set_darsfactor': funct:unur_tabl_set_darsfactor.

    `guidefactor = <double>'
          => *note `unur_tabl_set_guidefactor': funct:unur_tabl_set_guidefactor.

    `max_intervals [= <int>]'
          => *note `unur_tabl_set_max_intervals':
          funct:unur_tabl_set_max_intervals.

    `max_sqhratio = <double>'
          => *note `unur_tabl_set_max_sqhratio':
          funct:unur_tabl_set_max_sqhratio.

    `nstp [= <int>]'
          => *note `unur_tabl_set_nstp': funct:unur_tabl_set_nstp.

    `pedantic [= <int>]'
          => *note `unur_tabl_set_pedantic': funct:unur_tabl_set_pedantic.

    `slopes = (<list>), <int>'
          => *note `unur_tabl_set_slopes': funct:unur_tabl_set_slopes.

    `usedars [= <int>]'
          => *note `unur_tabl_set_usedars': funct:unur_tabl_set_usedars.

    `useear [= <int>]'
          => *note `unur_tabl_set_useear': funct:unur_tabl_set_useear.

    `variant_ia [= <int>]'
          => *note `unur_tabl_set_variant_ia': funct:unur_tabl_set_variant_ia.

    `variant_splitmode = <unsigned>'
          => *note `unur_tabl_set_variant_splitmode':
          funct:unur_tabl_set_variant_splitmode.

    `verify [= <int>]'
          => *note `unur_tabl_set_verify': funct:unur_tabl_set_verify.


   * `method = tdr'      => `unur_tdr_new' (*note TDR::)
    `c = <double>'
          => *note `unur_tdr_set_c': funct:unur_tdr_set_c.

    `cpoints = <int> [, (<list>)] | (<list>)'
          => *note `unur_tdr_set_cpoints': funct:unur_tdr_set_cpoints.

    `darsfactor = <double>'
          => *note `unur_tdr_set_darsfactor': funct:unur_tdr_set_darsfactor.

    `guidefactor = <double>'
          => *note `unur_tdr_set_guidefactor': funct:unur_tdr_set_guidefactor.

    `max_intervals [= <int>]'
          => *note `unur_tdr_set_max_intervals':
          funct:unur_tdr_set_max_intervals.

    `max_sqhratio = <double>'
          => *note `unur_tdr_set_max_sqhratio': funct:unur_tdr_set_max_sqhratio.

    `pedantic [= <int>]'
          => *note `unur_tdr_set_pedantic': funct:unur_tdr_set_pedantic.

    `reinit_ncpoints [= <int>]'
          => *note `unur_tdr_set_reinit_ncpoints':
          funct:unur_tdr_set_reinit_ncpoints.

    `reinit_percentiles = <int> [, (<list>)] | (<list>)'
          => *note `unur_tdr_set_reinit_percentiles':
          funct:unur_tdr_set_reinit_percentiles.

    `usecenter [= <int>]'
          => *note `unur_tdr_set_usecenter': funct:unur_tdr_set_usecenter.

    `usedars [= <int>]'
          => *note `unur_tdr_set_usedars': funct:unur_tdr_set_usedars.

    `usemode [= <int>]'
          => *note `unur_tdr_set_usemode': funct:unur_tdr_set_usemode.

    `variant_gw'
          => *note `unur_tdr_set_variant_gw': funct:unur_tdr_set_variant_gw.

    `variant_ia'
          => *note `unur_tdr_set_variant_ia': funct:unur_tdr_set_variant_ia.

    `variant_ps'
          => *note `unur_tdr_set_variant_ps': funct:unur_tdr_set_variant_ps.

    `verify [= <int>]'
          => *note `unur_tdr_set_verify': funct:unur_tdr_set_verify.


   * `method = utdr'      => `unur_utdr_new' (*note UTDR::)
    `cpfactor = <double>'
          => *note `unur_utdr_set_cpfactor': funct:unur_utdr_set_cpfactor.

    `deltafactor = <double>'
          => *note `unur_utdr_set_deltafactor': funct:unur_utdr_set_deltafactor.

    `pdfatmode = <double>'
          => *note `unur_utdr_set_pdfatmode': funct:unur_utdr_set_pdfatmode.

    `verify [= <int>]'
          => *note `unur_utdr_set_verify': funct:unur_utdr_set_verify.


   * `method = vempk'      => `unur_vempk_new' (*note VEMPK::)
    `smoothing = <double>'
          => *note `unur_vempk_set_smoothing': funct:unur_vempk_set_smoothing.

    `varcor [= <int>]'
          => *note `unur_vempk_set_varcor': funct:unur_vempk_set_varcor.


   * `method = vnrou'      => `unur_vnrou_new' (*note VNROU::)
    `r = <double>'
          => *note `unur_vnrou_set_r': funct:unur_vnrou_set_r.

    `v = <double>'
          => *note `unur_vnrou_set_v': funct:unur_vnrou_set_v.

    `verify [= <int>]'
          => *note `unur_vnrou_set_verify': funct:unur_vnrou_set_verify.


3.5 Uniform RNG String
======================

The value of the `urng' key is passed to the PRNG interface (see *Note
Overview: (prng)Top.  for details).  However it only works when using the PRNG
library is enabled, see *note Installation:: for details. There are no other
keys.

   IMPORTANT: UNU.RAN creates a new uniform random number generator for the
generator object. The pointer to this uniform generator has to be read and
saved via a `unur_get_urng' call in order to clear the memory _before_ the
UNU.RAN generator object is destroyed.

   If this block is omitted the UNU.RAN default generator is used (which _must
not_ be destroyed).

4 Handling distribution objects
*******************************

Objects of type `UNUR_DISTR' are used for handling distributions. All data
about a distribution are stored in this object. UNU.RAN provides functions that
return instances of such objects for standard distributions (*note Standard
distributions: Stddist.).  It is then possible to change these distribution
objects by various set calls. Moreover, it is possible to build a distribution
object entirely from scratch. For this purpose there exists
`unur_distr_<type>_new' calls that return an empty object of this type for each
object type (eg. univariate contiuous) which can be filled with the appropriate
set calls.

   UNU.RAN distinguishes between several types of distributions, each of which
has its own sets of possible parameters (for details see the corresponding
sections):
   - continuous univariate distributions

   - continuous univariate order statistics

   - continuous empirical univariate distributions

   - continuous multivariate distributions

   - continuous empirical multivariate distributions

   - matrix distributions

   - discrete univariate distributions

   Notice that there are essential data about a distribution, eg. the PDF, a
list of (shape, scale, location) parameters for the distribution, and the
domain of (the possibly truncated) distribution. And there exist parameters
that are/can be derived from these, eg. the mode of the distribution or the area
below the given PDF (which need not be normalized for many methods). UNU.RAN
keeps track of parameters which are known. Thus if one of the essential
parameters is changed all derived parameters are marked as unknown and must be
set again if these are required for the chosen generation method.  Additionally
to set calls there are calls for updating derived parameters for objects
provided by the UNU.RAN library of standard distributions (one for each
parameter to avoid computational overhead since not all parameters are required
for all generator methods).

   All parameters of distribution objects can be read by corresponding get
calls.

   Every generator object has its own copy of a distribution object which is
accessible by a `unur_get_distr' call. Thus the parameter for this distribution
can be read. However, *never* extract the distribution object out of a
generator object and run one of the set calls on it to modify the distribution.
(How should the poor generator object know what has happend?) Instead there
exist calls for each of the generator methods that change particular parameters
of the internal copy of the distribution object.

How To Use
..........

UNU.RAN collects all data required for a particular generation method in a
_distribution object_. There are two ways to get an instance of a distributions
object:
  1. Build a distribtion from scratch, by means of the corresponding
     `unur_distr_<type>_new' call, where `<type>' is the type of the
     distribution as listed in the below subsections.

  2. Use the corresponding `unur_distr_<name>_new' call to get prebuild
     distribution from the UNU.RAN library of standard distributions.  Here
     `<name>' is the name of the standard distribution in *note Standard
     distributions: Stddist.

   In either cases the corresponding `unur_distr_<type>_set_<param>' calls to
set the necessary parameters `<param>' (case 1), or change the values of the
standard distribution in case 2 (if this makes sense for you). In the latter
case `<type>' is the type to which the standard distribution belongs to.  These
`set' calls return `UNUR_SUCCESS' when the correspondig parameter has been set
successfully. Otherwise an error code is returned.

   The parameters of a distribution are divided into _essential_ and _derived_
parameters.

   Notice, that there are some restrictions in setting parameters to avoid
possible confusions.  Changing essential parameters marks derived parameters as
`unknown'. Some of the parameters cannot be changed any more when already set;
some parameters block each others.  In such a case a new instance of a
distribution object has to be build.

   Additionally `unur_distr_<type>_upd_<param>' calls can be used for updating
derived parameters for objects provided by the UNU.RAN library of standard
distributions.

   All parameters of a distribution object get be read by means of
`unur_distr_<type>_get_<param>' calls.

   Every distribution object be identified by its `name' which is a string of
arbitrary characters provided by the user. For standard distribution it is
automatically set to `<name>' in the corresponding `new' call. It can be
changed to any other string.

4.1 Functions for all kinds of distribution objects
===================================================

The calls in this section can be applied to all distribution objects.

   - Destroy `free' an instance of a generator object.

   - Ask for the `type' of a generator object.

   - Ask for the `dimension' of a generator object.

   - Deal with the `name' (identifier string) of a generator object.

Function reference
------------------

 -- Function: void unur_distr_free (UNUR_DISTR* DISTRIBUTION)
     Destroy the DISTRIBUTION object.

 -- Function: int unur_distr_set_name (UNUR_DISTR* DISTRIBUTION, const CHAR*
          NAME)
 -- Function: const char* unur_distr_get_name (const UNUR_DISTR* DISTRIBUTION)
     Set and get NAME of DISTRIBUTION. The NAME can be an arbitrary character
     string. It can be used to identify generator objects for the user. It is
     used by UNU.RAN when printing information of the distribution object into
     a log files.

 -- Function: int unur_distr_get_dim (const UNUR_DISTR* DISTRIBUTION)
     Get number of components of a random vector (its dimension) the
     DISTRIBUTION.

     For univariate distributions it returns dimension `1'.

     For matrix distributions it returns the number of components (i.e., number
     of rows times number of columns).  When the respective numbers of rows and
     columns are needed use `unur_distr_matr_get_dim' instead.

 -- Function: unsigned int unur_distr_get_type (const UNUR_DISTR* DISTRIBUTION)
     Get type of DISTRIBUTION.  Possible types are
    `UNUR_DISTR_CONT'
          univariate continuous distribution

    `UNUR_DISTR_CEMP'
          empirical continuous univariate distribution (i.e. a sample)

    `UNUR_DISTR_CVEC'
          continuous mulitvariate distribution

    `UNUR_DISTR_CVEMP'
          empirical continuous multivariate distribution (i.e. a vector sample)

    `UNUR_DISTR_DISCR'
          discrete univariate distribution

    `UNUR_DISTR_MATR'
          matrix distribution

     Alternatively the `unur_distr_is_<TYPE>' calls can be used.

 -- Function: int unur_distr_is_cont (const UNUR_DISTR* DISTRIBUTION)
     `TRUE' if DISTRIBUTION is a continuous univariate distribution.

 -- Function: int unur_distr_is_cvec (const UNUR_DISTR* DISTRIBUTION)
     `TRUE' if DISTRIBUTION is a continuous multivariate distribution.

 -- Function: int unur_distr_is_cemp (const UNUR_DISTR* DISTRIBUTION)
     `TRUE' if DISTRIBUTION is an empirical continuous univariate distribution,
     i.e. a sample.

 -- Function: int unur_distr_is_cvemp (const UNUR_DISTR* DISTRIBUTION)
     `TRUE' if DISTRIBUTION is an empirical continuous multivariate
     distribution.

 -- Function: int unur_distr_is_discr (const UNUR_DISTR* DISTRIBUTION)
     `TRUE' if DISTRIBUTION is a discrete univariate distribution.

 -- Function: int unur_distr_is_matr (const UNUR_DISTR* DISTRIBUTION)
     `TRUE' if DISTRIBUTION is a matrix distribution.

 -- Function: int unur_distr_set_extobj (UNUR_DISTR* DISTRIBUTION, const VOID*
          EXTOBJ)
     Store a pointer to an external object. This might be usefull if the PDF,
     PMF, CDF or other functions used to implement a particular distribution a
     parameter set that cannot be stored as doubles (e.g. pointers to some
     structure that holds information of the distribution).

     *Important:* When UNU.RAN copies this distribution object into the
     generator object, then the address EXTOBJ that this pointer contains is
     simply copied.  Thus the generator holds an address of a non-private
     object!  Once the generator object has been created any change in the
     external object might effect the generator object.

     *Warning:* External objects must be used with care. Once the generator
     object has been created or the distribution object has been copied you
     _must not_ destroy this external object.

 -- Function: const void* unur_distr_get_extobj (const UNUR_DISTR* DISTRIBUTION)
     Get the pointer to the external object.

     _Important:_ Changing this object must be done with with extreme care.

4.2 Continuous univariate distributions
=======================================

The calls in this section can be applied to continuous univariate distributions.

   - Create a `new' instance of a continuous univariate distribution.

   - Handle and evaluate distribution function (CDF, `cdf'), probability
     density function (PDF, `pdf') and the derivative of the density function
     (`dpdf').  The following is important:
        . `pdf' need not be normalized, i.e., any integrable nonnegative
          function can be used.

        . `dpdf' must the derivate of the function provided as `pdf'.

        . `cdf' must be a distribution function, i.e. it must be monotonically
          increasing with range [0,1].

        . If `cdf' and `pdf' are used together for a pariticular generation
          method, then `pdf' must be the derivate of the `cdf', i.e., it must
          be normalized.

   - Handle and evaluate the logarithm of the probability density function
     (logPDF, `logpdf') and the derivative of the logarithm of the density
     function (`dlogpdf').

     Some methods use the logarithm of the density if available.

   - Set (and change) parameters (`pdfparams') and the area below the graph
     (`pdfarea') of the given density.

   - Set the `mode' (or pole) of the distribution.

   - Set the `center' of the distribution.  It is used by some generation
     methods to adjust the parameters of the generation algorithms to gain
     better performance. It can be seens as the location of the "central part"
     of the distribution.

   - Some generation methods require the hazard rate (`hr') of the distribution
     instead of its `pdf'.

   - Alternatively, `cdf', `pdf', `dpdf', and `hr' can be provided as `str'ings
     instead of function pointers.

   - Set the `domain' of the distribution.  Notice that the library also can
     handle truncated distributions, i.e., distributions that are derived from
     (standard) distributions by simply restricting its domain to a subset.
     However, there is a subtle difference between changing the domain of a
     distribution object by a `unur_distr_cont_set_domain' call and changing the
     (truncated) domain for an existing generator object. The domain of the
     distribution object is used to create the generator object with hats,
     squeezes, tables, etc.  Whereas truncating the domain of an existing
     generator object need not necessarily require a recomputation of these
     data.  Thus by a `unur_<method>_chg_truncated' call (if available) the
     sampling region is restricted to the subset of the domain of the given
     distribution object. However, generation methods that require a recreation
     of the generator object when the domain is changed have a
     `unur_<method>_chg_domain' call instead.  For these calls there are of
     course no restrictions on the given domain (i.e., it is possible to
     increase the domain of the distribution) (*note Methods::, for details).


Function reference
------------------

 -- Function: UNUR_DISTR* unur_distr_cont_new (void)
     Create a new (empty) object for univariate continuous distribution.

Essential parameters
....................

 -- Function: int unur_distr_cont_set_pdf (UNUR_DISTR* DISTRIBUTION,
          UNUR_FUNCT_CONT* PDF)
 -- Function: int unur_distr_cont_set_dpdf (UNUR_DISTR* DISTRIBUTION,
          UNUR_FUNCT_CONT* DPDF)
 -- Function: int unur_distr_cont_set_cdf (UNUR_DISTR* DISTRIBUTION,
          UNUR_FUNCT_CONT* CDF)
 -- Function: int unur_distr_cont_set_invcdf (UNUR_DISTR* DISTRIBUTION,
          UNUR_FUNCT_CONT* INVCDF)
     Set respective pointer to the probability density function (PDF), the
     derivative of the probability density function (dPDF), the cumulative
     distribution function (CDF), and the inverse CDF of the DISTRIBUTION.
     Each of these function pointers must be of type `double funct(double x,
     const UNUR_DISTR *distr)'.

     Due to the fact that some of the methods do not require a normalized PDF
     the following is important:

        - The given CDF must be the cumulative distribution function of the
          (non-truncated) distribution. If a distribution from the UNU.RAN
          library of standard distributions (*note Standard distributions:
          Stddist.)  is truncated, there is no need to change the CDF.

        - If both the CDF and the PDF are used (for a method or for order
          statistics), the PDF must be the derivative of the CDF.  If a
          truncated distribution for one of the standard distributions from the
          UNU.RAN library of standard distributions is used, there is no need
          to change the PDF.

        - If the area below the PDF is required for a given distribution it
          must be given by the `unur_distr_cont_set_pdfarea' call.  For a
          truncated distribution this must be of course the integral of the PDF
          in the given truncated domain.  For distributions from the UNU.RAN
          library of standard distributions this is done automatically by the
          `unur_distr_cont_upd_pdfarea' call.


     It is important to note that all these functions must return a result for
     all values of X. Eg., if the domain of a given PDF is the interval [-1,1],
     then the given function must return `0.0' for all points outside this
     interval.  In case of an overflow the PDF should return `UNUR_INFINITY'.

     It is not possible to change such a function. Once the PDF or CDF is set
     it cannot be overwritten. This also holds when the logPDF is given or when
     the PDF is given by the `unur_distr_cont_set_pdfstr' or
     `unur_distr_cont_set_logpdfstr' call.  A new distribution object has to be
     used instead.

 -- Function: UNUR_FUNCT_CONT* unur_distr_cont_get_pdf (const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: UNUR_FUNCT_CONT* unur_distr_cont_get_dpdf (const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: UNUR_FUNCT_CONT* unur_distr_cont_get_cdf (const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: UNUR_FUNCT_CONT* unur_distr_cont_get_invcdf (const UNUR_DISTR*
          DISTRIBUTION)
     Get the respective pointer to the PDF, the derivative of the PDF, the CDF,
     and the inverse CDF of the DISTRIBUTION. The pointer is of type `double
     funct(double x, const UNUR_DISTR *distr)'.  If the corresponding function
     is not available for the distribution, the `NULL' pointer is returned.

 -- Function: double unur_distr_cont_eval_pdf (double X, const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: double unur_distr_cont_eval_dpdf (double X, const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: double unur_distr_cont_eval_cdf (double X, const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: double unur_distr_cont_eval_invcdf (double U, const UNUR_DISTR*
          DISTRIBUTION)
     Evaluate the PDF, derivative of the PDF, the CDF, and the inverse CDF at X
     and U,respectively.  Notice that DISTRIBUTION must not be the `NULL'
     pointer.  If the corresponding function is not available for the
     distribution, `UNUR_INFINITY' is returned and `unur_errno' is set to
     `UNUR_ERR_DISTR_DATA'.

     _IMPORTANT:_ In the case of a truncated standard distribution these calls
     always return the respective values of the _untruncated_ distribution!

 -- Function: int unur_distr_cont_set_logpdf (UNUR_DISTR* DISTRIBUTION,
          UNUR_FUNCT_CONT* LOGPDF)
 -- Function: int unur_distr_cont_set_dlogpdf (UNUR_DISTR* DISTRIBUTION,
          UNUR_FUNCT_CONT* DLOGPDF)
 -- Function: int unur_distr_cont_set_logcdf (UNUR_DISTR* DISTRIBUTION,
          UNUR_FUNCT_CONT* LOGCDF)
 -- Function: UNUR_FUNCT_CONT* unur_distr_cont_get_logpdf (const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: UNUR_FUNCT_CONT* unur_distr_cont_get_dlogpdf (const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: UNUR_FUNCT_CONT* unur_distr_cont_get_logcdf (const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: double unur_distr_cont_eval_logpdf (double X, const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: double unur_distr_cont_eval_dlogpdf (double X, const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: double unur_distr_cont_eval_logcdf (double X, const UNUR_DISTR*
          DISTRIBUTION)
     Analogous calls for the logarithm of the density distribution functions.

 -- Function: int unur_distr_cont_set_pdfstr (UNUR_DISTR* DISTRIBUTION, const
          CHAR* PDFSTR)
     This function provides an alternative way to set a PDF and its derivative
     of the DISTRIBUTION.  PDFSTR is a character string that contains the
     formula for the PDF, see *note Function String: StringFunct, for details.
     The derivative of the given PDF is computed automatically.  See also the
     remarks for the `unur_distr_cont_set_pdf' call.

     It is not possible to call this funtion twice or to call this function
     after a `unur_distr_cont_set_pdf' call.

 -- Function: int unur_distr_cont_set_cdfstr (UNUR_DISTR* DISTRIBUTION, const
          CHAR* CDFSTR)
     This function provides an alternative way to set a CDF; analogously to the
     `unur_distr_cont_set_pdfstr' call.  The PDF and its derivative of the
     given CDF are computed automatically.

 -- Function: char* unur_distr_cont_get_pdfstr (const UNUR_DISTR* DISTRIBUTION)
 -- Function: char* unur_distr_cont_get_dpdfstr (const UNUR_DISTR* DISTRIBUTION)
 -- Function: char* unur_distr_cont_get_cdfstr (const UNUR_DISTR* DISTRIBUTION)
     Get pointer to respective string for PDF, derivate of PDF, and CDF of
     DISTRIBUTION that is given as string (instead of a function pointer).
     This call allocates memory to produce this string. It should be freed when
     it is not used any more.

 -- Function: int unur_distr_cont_set_pdfparams (UNUR_DISTR* DISTRIBUTION,
          const DOUBLE* PARAMS, int N_PARAMS)
     Sets array of parameters for DISTRIBUTION. There is an upper limit for the
     number of parameters `n_params'. It is given by the macro
     `UNUR_DISTR_MAXPARAMS' in `unuran_config.h'. (It is set to 5 by default
     but can be changed to any appropriate nonnegative number.)  If N_PARAMS is
     negative or exceeds this limit no parameters are copied into the
     distribution object and `unur_errno' is set to `UNUR_ERR_DISTR_NPARAMS'.

     For standard distributions from the UNU.RAN library the parameters are
     checked. Moreover, the domain is updated automatically unless it has been
     changed before by a `unur_distr_cont_set_domain' call.  If the given
     parameters are invalid for the standard distribution, then no parameters
     are set and an error code is returned.  Notice, that the given parameter
     list for such a distribution is handled in the same way as in the
     corresponding `new' calls, i.e. optional parameters for the PDF that are
     not present in the given list are (re-)set to their default values.

     *Important:* If the parameters of a distribution from the UNU.RAN library
     of standard distributions (*note Standard distributions: Stddist.)  are
     changed, then neither its mode nor the normalization constant are updated.
     Please use the respective calls `unur_distr_cont_upd_mode' and
     `unur_distr_cont_upd_pdfarea'.  Moreover, if the domain has been changed
     by a `unur_distr_cont_set_domain' it is not automatically updated, either.
     Updating the normalization constant is in particular very important, when
     the CDF of the distribution is used.

 -- Function: int unur_distr_cont_get_pdfparams (const UNUR_DISTR*
          DISTRIBUTION, const DOUBLE** PARAMS)
     Get number of parameters of the PDF and set pointer PARAMS to array of
     parameters. If no parameters are stored in the object, an error code is
     returned and `params' is set to `NULL'.

     _Important:_ Do *not* change the entries in PARAMS!

 -- Function: int unur_distr_cont_set_pdfparams_vec (UNUR_DISTR* DISTRIBUTION,
          int PAR, const DOUBLE* PARAM_VEC, int N_PARAM_VEC)
     This function provides an interface for additional vector parameters for a
     continuous DISTRIBUTION.

     It sets the parameter with number PAR.  PAR indicates directly which of
     the parameters is set and must be a number between `0' and
     `UNUR_DISTR_MAXPARAMS'-1 (the upper limit of possible parameters defined in
     `unuran_config.h'; it is set to 5 but can be changed to any appropriate
     nonnegative number.)

     The entries of a this parameter are given by the array PARAM_VEC of size
     N_PARAM_VEC.

     If PARAM_VEC is `NULL' then the corresponding entry is cleared.

     If an error occurs no parameters are copied into the parameter object
     `unur_errno' is set to `UNUR_ERR_DISTR_DATA'.

 -- Function: int unur_distr_cont_get_pdfparams_vec (const UNUR_DISTR*
          DISTRIBUTION, int PAR, const DOUBLE** PARAM_VECS)
     Get parameter of the PDF with number PAR.  The pointer to the parameter
     array is stored in PARAM_VECS, its size is returned by the function.  If
     the requested parameter is not set, then an error code is returned and
     `params' is set to `NULL'.

     _Important:_ Do *not* change the entries in PARAM_VECS!

 -- Function: int unur_distr_cont_set_logpdfstr (UNUR_DISTR* DISTRIBUTION,
          const CHAR* LOGPDFSTR)
 -- Function: char* unur_distr_cont_get_logpdfstr (const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: char* unur_distr_cont_get_dlogpdfstr (const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: int unur_distr_cont_set_logcdfstr (UNUR_DISTR* DISTRIBUTION,
          const CHAR* LOGCDFSTR)
 -- Function: char* unur_distr_cont_get_logcdfstr (const UNUR_DISTR*
          DISTRIBUTION)
     Analogous calls for the logarithm of the density and distribution
     functions.

 -- Function: int unur_distr_cont_set_domain (UNUR_DISTR* DISTRIBUTION, double
          LEFT, double RIGHT)
     Set the left and right borders of the domain of the distribution. This can
     also be used to truncate an existing distribution. For setting the
     boundary to +/- infinity use `+/- UNUR_INFINITY'.  If RIGHT is not
     strictly greater than LEFT no domain is set and `unur_errno' is set to
     `UNUR_ERR_DISTR_SET'.

     _Important:_ For some technical reasons it is assumed that the density is
     unimodal and thus monotone on either side of the mode! This is used in the
     case when the given mode is outside of the original domain. Then the mode
     is set to the corresponding boundary of the new domain.  If this result is
     not the desired it must be changed by using a `unur_distr_cont_set_mode'
     call (or a `unur_distr_cont_upd_mode' call). The same holds for the center
     of the distribution.

 -- Function: int unur_distr_cont_get_domain (const UNUR_DISTR* DISTRIBUTION,
          double* LEFT, double* RIGHT)
     Get the left and right borders of the domain of the distribution. If the
     domain is not set `+/- UNUR_INFINITY' is assumed and returned. No error is
     reported in this case.

 -- Function: int unur_distr_cont_get_truncated (const UNUR_DISTR*
          DISTRIBUTION, double* LEFT, double* RIGHT)
     Get the left and right borders of the (truncated) domain of the
     distribution. For non-truncated distribution this call is equivalent to the
     `unur_distr_cont_get_domain' call.

     This call is only useful in connection with a `unur_get_distr' call to get
     the boundaries of the sampling region of a generator object.

 -- Function: int unur_distr_cont_set_hr (UNUR_DISTR* DISTRIBUTION,
          UNUR_FUNCT_CONT* HAZARD)
     Set pointer to the hazard rate (HR) of the DISTRIBUTION.

     The _hazard rate_ (or failure rate) is a mathematical way of describing
     aging. If the lifetime X is a random variable with density f(x) and CDF
     F(x) the hazard rate h(x) is defined as h(x) = f(x) / (1-F(x)).  In other
     words, h(x) represents the (conditional) rate of failure of a unit that
     has survived up to time x with probability 1-F(x).  The key distribution
     is the exponential distribution as it has constant hazard rate of value 1.
     Hazard rates tending to infinity describe distributions with
     sub-exponential tails whereas distributions with hazard rates tending to
     zero have heavier tails than the exponential distribution.

     It is important to note that all these functions must return a result for
     all floats x. In case of an overflow the PDF should return `UNUR_INFINITY'.

     *Important*: Do not simply use f(x) / (1-F(x)), since this is numerically
     very unstable and results in numerical noise if F(x) is (very) close to 1.
     Moreover, if the density f(x) is known a generation method that uses the
     density is more appropriate.

     It is not possible to change such a function. Once the HR is set it cannot
     be overwritten. This also holds when the HR is given by the
     `unur_distr_cont_set_hrstr' call. A new distribution object has to be used
     instead.

 -- Function: UNUR_FUNCT_CONT* unur_distr_cont_get_hr (const UNUR_DISTR*
          DISTRIBUTION)
     Get the pointer to the hazard rate of the DISTRIBUTION. The pointer is of
     type `double funct(double x, const UNUR_DISTR *distr)'.  If the
     corresponding function is not available for the distribution, the `NULL'
     pointer is returned.

 -- Function: double unur_distr_cont_eval_hr (double X, const UNUR_DISTR*
          DISTRIBUTION)
     Evaluate the hazard rate at X.  Notice that DISTRIBUTION must not be the
     `NULL' pointer.  If the corresponding function is not available for the
     distribution, `UNUR_INFINITY' is returned and `unur_errno' is set to
     `UNUR_ERR_DISTR_DATA'.

 -- Function: int unur_distr_cont_set_hrstr (UNUR_DISTR* DISTRIBUTION, const
          CHAR* HRSTR)
     This function provides an alternative way to set a hazard rate and its
     derivative of the DISTRIBUTION.  HRSTR is a character string that contains
     the formula for the HR, see *note Function String: StringFunct, for
     details.  See also the remarks for the `unur_distr_cont_set_hr' call.

     It is not possible to call this funtion twice or to call this function
     after a `unur_distr_cont_set_hr' call.

 -- Function: char* unur_distr_cont_get_hrstr (const UNUR_DISTR* DISTRIBUTION)
     Get pointer to string for HR of DISTRIBUTION that is given via the string
     interface. This call allocates memory to produce this string. It should be
     freed when it is not used any more.

Derived parameters
..................

The following paramters *must* be set whenever one of the essential parameters
has been set or changed (and the parameter is required for the chosen method).

 -- Function: int unur_distr_cont_set_mode (UNUR_DISTR* DISTRIBUTION, double
          MODE)
     Set mode of DISTRIBUTION. The MODE must be contained in the domain of
     DISTRIBUTION. Otherwise the mode is not set and `unur_errno' is set to
     `UNUR_ERR_DISTR_SET'.  For distributions with unbounded density, this call
     is used to set the pole of the PDF. Notice that the PDF should then return
     UNUR_INFINITY at the pole.  Notice that the mode is adjusted when the
     domain is set, see the remark for the `unur_distr_cont_set_domain' call.

 -- Function: int unur_distr_cont_upd_mode (UNUR_DISTR* DISTRIBUTION)
     Recompute the mode of the DISTRIBUTION. This call works properly for
     distribution objects from the UNU.RAN library of standard distributions
     when the corresponding function is available.  Otherwise a (slow)
     numerical mode finder based on Brent's algorithm is used. If it failes
     `unur_errno' is set to `UNUR_ERR_DISTR_DATA'.

 -- Function: double unur_distr_cont_get_mode (UNUR_DISTR* DISTRIBUTION)
     Get mode of DISTRIBUTION. If the mode is not marked as known,
     `unur_distr_cont_upd_mode' is called to compute the mode. If this is not
     successful `UNUR_INFINITY' is returned and `unur_errno' is set to
     `UNUR_ERR_DISTR_GET'.  (There is no difference between the case where no
     routine for computing the mode is available and the case where no mode
     exists for the distribution at all.)

 -- Function: int unur_distr_cont_set_center (UNUR_DISTR* DISTRIBUTION, double
          CENTER)
     Set center of the DISTRIBUTION. The center is used by some methods to
     shift the distribution in order to decrease numerical round-off error. If
     not given explicitly a default is used.

     _Important:_ This call does not check whether the center is contained in
     the given domain.

     Default: The mode, if set by a `unur_distr_cont_set_mode' or
     `unur_distr_cont_upd_mode' call; otherwise `0'.

 -- Function: double unur_distr_cont_get_center (const UNUR_DISTR* DISTRIBUTION)
     Get center of the DISTRIBUTION. It always returns some point as there
     always exists a default for the center, see `unur_distr_cont_set_center'.

 -- Function: int unur_distr_cont_set_pdfarea (UNUR_DISTR* DISTRIBUTION, double
          AREA)
     Set the area below the PDF. If `area' is non-positive, no area is set and
     `unur_errno' is set to `UNUR_ERR_DISTR_SET'.

     For a distribution object created by the UNU.RAN library of standard
     distributions you always should use the `unur_distr_cont_upd_pdfarea'.
     Otherwise there might be ambiguous side-effects.

 -- Function: int unur_distr_cont_upd_pdfarea (UNUR_DISTR* DISTRIBUTION)
     Recompute the area below the PDF of the distribution.  It only works for
     distribution objects from the UNU.RAN library of standard distributions
     when the corresponding function is available. Otherwise `unur_errno' is
     set to `UNUR_ERR_DISTR_DATA'.

     This call also sets the normalization constant such that the given PDF is
     the derivative of a given CDF, i.e. the area is 1.  However, for truncated
     distributions the area is smaller than 1.

     The call does not work for distributions from the UNU.RAN library of
     standard distributions with truncated domain when the CDF is not available.

 -- Function: double unur_distr_cont_get_pdfarea (UNUR_DISTR* DISTRIBUTION)
     Get the area below the PDF of the distribution. If this area is not known,
     `unur_distr_cont_upd_pdfarea' is called to compute it. If this is not
     successful `UNUR_INFINITY' is returned and `unur_errno' is set to
     `UNUR_ERR_DISTR_GET'.

4.3 Continuous univariate order statistics
==========================================

These are special cases of a continuous univariate distributions and thus they
have most of these parameters (with the exception that functions cannot be
changed). Additionally,

   - there is a call to extract the underlying distribution,

   - and a call to handle the `rank' of the order statistics.


Function reference
------------------

 -- Function: UNUR_DISTR* unur_distr_corder_new (const UNUR_DISTR*
          DISTRIBUTION, int N, int K)
     Create an object for order statistics of sample size N and rank K.
     DISTRIBUTION must be a pointer to a univariate continuous distribution.
     The resulting generator object is of the same type as of a
     `unur_distr_cont_new' call.  (However, it cannot be used to make an order
     statistics out of an order statistics.)

     To have a PDF for the order statistics, the given distribution object must
     contain a CDF and a PDF. Moreover, it is assumed that the given PDF is the
     derivative of the given CDF. Otherwise the area below the PDF of the order
     statistics is not computed correctly.

     _Important:_ There is no warning when the computed area below the PDF of
     the order statistics is wrong.

 -- Function: const UNUR_DISTR* unur_distr_corder_get_distribution (const
          UNUR_DISTR* DISTRIBUTION)
     Get pointer to distribution object for underlying distribution.

Essential parameters
....................

 -- Function: int unur_distr_corder_set_rank (UNUR_DISTR* DISTRIBUTION, int N,
          int K)
     Change sample size N and rank K of order statistics.  In case of invalid
     data, no parameters are changed.  The area below the PDF can be set to
     that of the underlying distribution by a `unur_distr_corder_upd_pdfarea'
     call.

 -- Function: int unur_distr_corder_get_rank (const UNUR_DISTR* DISTRIBUTION,
          int* N, int* K)
     Get sample size N and rank K of order statistics.  In case of error an
     error code is returned.

Additionally most of the set and get calls for continuous univariate
distributions work. The most important exceptions are that the PDF and CDF
cannot be changed and `unur_distr_cont_upd_mode' uses in any way a (slow)
numerical method that might fail.

 -- Function: UNUR_FUNCT_CONT* unur_distr_corder_get_pdf (UNUR_DISTR*
          DISTRIBUTION)
 -- Function: UNUR_FUNCT_CONT* unur_distr_corder_get_dpdf (UNUR_DISTR*
          DISTRIBUTION)
 -- Function: UNUR_FUNCT_CONT* unur_distr_corder_get_cdf (UNUR_DISTR*
          DISTRIBUTION)
     Get the respective pointer to the PDF, the derivative of the PDF and the
     CDF of the distribution, respectively. The pointer is of type `double
     funct(double x, UNUR_DISTR *distr)'.  If the corresponding function is not
     available for the distribution, the `NULL' pointer is returned.  See also
     `unur_distr_cont_get_pdf'.  (Macro)

 -- Function: double unur_distr_corder_eval_pdf (double X, UNUR_DISTR*
          DISTRIBUTION)
 -- Function: double unur_distr_corder_eval_dpdf (double X, UNUR_DISTR*
          DISTRIBUTION)
 -- Function: double unur_distr_corder_eval_cdf (double X, UNUR_DISTR*
          DISTRIBUTION)
     Evaluate the PDF, derivative of the PDF. and the CDF, respectively, at X.
     Notice that DISTRIBUTION must not be the `NULL' pointer.  If the
     corresponding function is not available for the distribution,
     `UNUR_INFINITY' is returned and `unur_errno' is set to
     `UNUR_ERR_DISTR_DATA'.  See also `unur_distr_cont_eval_pdf'.  (Macro)

     _IMPORTANT:_ In the case of a truncated standard distribution these calls
     always return the respective values of the _untruncated_ distribution!

 -- Function: int unur_distr_corder_set_pdfparams (UNUR_DISTR* DISTRIBUTION,
          double* PARAMS, int N_PARAMS)
     Set array of parameters for underlying distribution.  See
     `unur_distr_cont_set_pdfparams' for details.  (Macro)

 -- Function: int unur_distr_corder_get_pdfparams (UNUR_DISTR* DISTRIBUTION,
          double** PARAMS)
     Get number of parameters of the PDF of the underlying distribution and set
     pointer PARAMS to array of parameters.  See `unur_distr_cont_get_pdfparams'
     for details.  (Macro)

 -- Function: int unur_distr_corder_set_domain (UNUR_DISTR* DISTRIBUTION,
          double LEFT, double RIGHT)
     Set the left and right borders of the domain of the distribution.  See
     `unur_distr_cont_set_domain' for details.  (Macro)

 -- Function: int unur_distr_corder_get_domain (UNUR_DISTR* DISTRIBUTION,
          double* LEFT, double* RIGHT)
     Get the left and right borders of the domain of the distribution.  See
     `unur_distr_cont_get_domain' for details.  (Macro)

 -- Function: int unur_distr_corder_get_truncated (UNUR_DISTR* DISTRIBUTION,
          double* LEFT, double* RIGHT)
     Get the left and right borders of the (truncated) domain of the
     distribution.  See `unur_distr_cont_get_truncated' for details.  (Macro)

Derived parameters
..................

The following paramters *must* be set whenever one of the essential parameters
has been set or changed (and the parameter is required for the chosen method).

 -- Function: int unur_distr_corder_set_mode (UNUR_DISTR* DISTRIBUTION, double
          MODE)
     Set mode of distribution.  See also `unur_distr_corder_set_mode'.  (Macro)

 -- Function: double unur_distr_corder_upd_mode (UNUR_DISTR* DISTRIBUTION)
     Recompute the mode of the distribution numerically. Notice that this
     routine is slow and might not work properly in every case.  See also
     `unur_distr_cont_upd_mode' for further details.  (Macro)

 -- Function: double unur_distr_corder_get_mode (UNUR_DISTR* DISTRIBUTION)
     Get mode of distribution.  See `unur_distr_cont_get_mode' for details.
     (Macro)

 -- Function: int unur_distr_corder_set_pdfarea (UNUR_DISTR* DISTRIBUTION,
          double AREA)
     Set the area below the PDF.  See `unur_distr_cont_set_pdfarea' for details.
     (Macro)

 -- Function: double unur_distr_corder_upd_pdfarea (UNUR_DISTR* DISTRIBUTION)
     Recompute the area below the PDF of the distribution.  It only works for
     order statistics for distribution objects from the UNU.RAN library of
     standard distributions when the corresponding function is available.
     `unur_distr_cont_upd_pdfarea' assumes that the PDF of the underlying
     distribution is normalized, i.e. it is the derivative of its CDF.
     Otherwise the computed area is wrong and there is *no* warning about this
     failure.  See `unur_distr_cont_upd_pdfarea' for further details.  (Macro)

 -- Function: double unur_distr_corder_get_pdfarea (UNUR_DISTR* DISTRIBUTION)
     Get the area below the PDF of the distribution.  See
     `unur_distr_cont_get_pdfarea' for details.  (Macro)

4.4 Continuous empirical univariate distributions
=================================================

Empirical univariate distributions are derived from observed data.  There are
two ways to create such a generator object:
  1. By a list of _raw data_ by means of a `unur_distr_cemp_set_data' call.

  2. By a _histogram_ (i.e. preprocessed data) by means of a
     `unur_distr_cemp_set_hist' call.
        How these data are used to sample from the empirical distribution
depends from the chosen generation method.

Function reference
------------------

 -- Function: UNUR_DISTR* unur_distr_cemp_new (void)
     Create a new (empty) object for empirical univariate continuous
     distribution.

Essential parameters
....................

 -- Function: int unur_distr_cemp_set_data (UNUR_DISTR* DISTRIBUTION, const
          DOUBLE* SAMPLE, int N_SAMPLE)
     Set observed sample for empirical distribution.

 -- Function: int unur_distr_cemp_read_data (UNUR_DISTR* DISTRIBUTION, const
          CHAR* FILENAME)
     Read data from file `filename'.  It reads the first number from each line.
     Numbers are parsed by means of the C standard routine `strtod'.  Lines
     that do not start with `+', `-', `.', or a digit are ignored. (Beware of
     lines starting with a blank!)

     In case of an error (file cannot be opened, invalid string for double in
     line) no data are copied into the distribution object and an error code is
     returned.

 -- Function: int unur_distr_cemp_get_data (const UNUR_DISTR* DISTRIBUTION,
          const DOUBLE** SAMPLE)
     Get number of samples and set pointer SAMPLE to array of observations. If
     no sample has been given, an error code is returned and `sample' is set to
     `NULL'.

     _Important:_ Do *not* change the entries in SAMPLE!

 -- Function: int unur_distr_cemp_set_hist (UNUR_DISTR* DISTRIBUTION, const
          DOUBLE* PROB, int N_PROB, double XMIN, double XMAX)
     Set a histogram with bins of equal width. PROB is an array of length
     N_PROB that contains the probabilities for the bins (in ascending order).
     XMIN and XMAX give the lower and upper bound of the histogram,
     respectively. The bins are assumed to have equal width.

     _Remark:_ This is shortcut for calling `unur_distr_cemp_set_hist_prob' and
     `unur_distr_cemp_set_hist_domain'.  _Notice:_ All sampling methods either
     use raw data or histogram.  It is possible to set both types of data;
     however, it is not checked whether the given histogran corresponds to
     possibly given raw data.

 -- Function: int unur_distr_cemp_set_hist_prob (UNUR_DISTR* DISTRIBUTION,
          const DOUBLE* PROB, int N_PROB)
     Set probabilities of a histogram with N_PROB bins.  Hence PROB must be an
     array of length N_PROB that contains the probabilities for the bins in
     ascending order.  It is important also to set the location of the bins
     either with a `unur_distr_cemp_set_hist_domain' for bins of equal width or
     `unur_distr_cemp_set_hist_bins' when the bins have different width.

     _Notice:_ All sampling methods either use raw data or histogram.  It is
     possible to set both types of data; however, it is not checked whether the
     given histogram corresponds to possibly given raw data.

 -- Function: int unur_distr_cemp_set_hist_domain (UNUR_DISTR* DISTRIBUTION,
          double XMIN, double XMAX)
     Set a domain of a histogram with bins of equal width.  XMIN and XMAX give
     the lower and upper bound of the histogram, respectively.

 -- Function: int unur_distr_cemp_set_hist_bins (UNUR_DISTR* DISTRIBUTION,
          const DOUBLE* BINS, int N_BINS)
     Set location of bins of a histogram with N_BINS bins.  Hence BINS must be
     an array of length N_BINS.  The domain of the DISTRIBUTION is
     automatically set by this call and overrides any calls to
     `unur_distr_cemp_set_hist_domain'.  _Important:_ The probabilities of the
     bins of the DISTRIBUTION must be already be set by a
     `unur_distr_cemp_set_hist_prob' (or a `unur_distr_cemp_set_hist' call) and
     the value of N_BINS must equal N_PROB`+1' from the corresponding value of
     the respective call.

4.5 Continuous multivariate distributions
=========================================

The following calls handle multivariate distributions.  However, the
requirements of particular generation methods is not as unique as for
univariate distributions. Moreover, random vector generation methods are still
under development.  The below functions are a first attempt to handle this
situation.

   Notice that some of the parameters - when given carelessly - might
contradict to others. For example: Some methods require the marginal
distribution and some methods need a standardized form of the marginal
distributions, where the actual mean and variance is stored in the mean vector
and the covariance matrix, respectively.

   We also have to mention that some methods might abuse some of the
parameters. Please read the discription of the chosen sampling method carfully.

   The following kind of calls exists:

   - Create a `new' instance of a continuous multivariate distribution;

   - Handle and evaluate probability density function (PDF, `pdf') and the
     gradient of the density function (`dpdf').  The following is important:
        . `pdf' need not be normalized, i.e., any integrable nonnegative
          function can be used.

        . `dpdf' must the derivate of the function provided as `pdf'.

   - Handle and evaluate the logarithm of the probability density function
     (logPDF, `logpdf') and the gradient of the logarithm of the density
     function (`dlogpdf').

     Some methods use the logarithm of the density if available.

   - Set (and change) parameters (`pdfparams') and the volume below the graph
     (`pdfvol') of the given density.

   - Set `mode' and `mean' of the distribution.

   - Set the `center' of the distribution.  It is used by some generation
     methods to adjust the parameters of the generation algorithms to gain
     better performance. It can be seens as the location of the "central part"
     of the distribution.

   - Handle the `covar'iance matrix of the distribution and its `cholesky' and
     `inv'verse matrices.

   - Set the `rankcorr'elation matrix of the distribution.

   - Deal with `marginal' distributions.

   - Set domain of the distribution.


Function reference
------------------

 -- Function: UNUR_DISTR* unur_distr_cvec_new (int DIM)
     Create a new (empty) object for multivariate continuous distribution. DIM
     is the number of components of the random vector (i.e. its dimension). It
     is also possible to use dimension 1.  Notice, however, that this is
     treated as a distribution of random vectors with only one component and
     not as a distribution of real numbers. For the latter `unur_distr_cont_new'
     should be used to create an object for a univariate distribution.

Essential parameters
....................

 -- Function: int unur_distr_cvec_set_pdf (UNUR_DISTR* DISTRIBUTION,
          UNUR_FUNCT_CVEC* PDF)
     Set respective pointer to the PDF of the DISTRIBUTION.  This function must
     be of type `double funct(const double *x, UNUR_DISTR *distr)', where X
     must be a pointer to a double array of appropriate size (i.e. of the same
     size as given to the `unur_distr_cvec_new' call).

     It is not necessary that the given PDF is normalized, i.e. the integral
     need not be 1.  Nevertheless the volume below the PDF can be provided by a
     `unur_distr_cvec_set_pdfvol' call.

     It is not possible to change the PDF. Once the PDF is set it cannot be
     overwritten. This also holds when the logPDF is given.  A new distribution
     object has to be used instead.

 -- Function: int unur_distr_cvec_set_dpdf (UNUR_DISTR* DISTRIBUTION,
          UNUR_VFUNCT_CVEC* DPDF)
     Set pointer to the gradient of the PDF. The type of this function must be
     `int funct(double *result, const double *x, UNUR_DISTR *distr)', where
     RESULT and X must be pointers to double arrays of appropriate size (i.e.
     of the same size as given to the `unur_distr_cvec_new' call).  The
     gradient of the PDF is stored in the array RESULT.  The function should
     return an error code in case of an error and must return `UNUR_SUCCESS'
     otherwise.

     The given function must be the gradient of the function given by a
     `unur_distr_cvec_set_pdf' call.

     It is not possible to change the gradient of the PDF. Once the dPDF is set
     it cannot be overwritten. This also holds when the gradient of the logPDF
     is given.  A new distribution object has to be used instead.

 -- Function: int unur_distr_cvec_set_pdpdf (UNUR_DISTR* DISTRIBUTION,
          UNUR_FUNCTD_CVEC* PDPDF)
     Set pointer to partial derivatives of the PDF. The type of this function
     must be `double funct(const double *x, int coord, UNUR_DISTR *distr)',
     where X must be a pointer to a double array of appropriate size (i.e. of
     the same size as given to the `unur_distr_cvec_new' call). COORD is the
     coordinate for which the partial dervative should be computed.

     Notice that COORD must be an integer from {0,...,dim-1}.

     It is not possible to change the partial derivative of the PDF. Once the
     pdPDF is set it cannot be overwritten. This also holds when the partial
     derivative of the logPDF is given.  A new distribution object has to be
     used instead.

 -- Function: UNUR_FUNCT_CVEC* unur_distr_cvec_get_pdf (const UNUR_DISTR*
          DISTRIBUTION)
     Get the pointer to the PDF of the DISTRIBUTION. The pointer is of type
     `double funct(const double *x, UNUR_DISTR *distr)'.  If the corresponding
     function is not available for the DISTRIBUTION, the `NULL' pointer is
     returned.

 -- Function: UNUR_VFUNCT_CVEC* unur_distr_cvec_get_dpdf (const UNUR_DISTR*
          DISTRIBUTION)
     Get the pointer to the gradient of the PDF of the DISTRIBUTION. The
     pointer is of type `int double funct(double *result, const double *x,
     UNUR_DISTR *distr)'.  If the corresponding function is not available for
     the DISTRIBUTION, the `NULL' pointer is returned.

 -- Function: double unur_distr_cvec_eval_pdf (const DOUBLE* X, UNUR_DISTR*
          DISTRIBUTION)
     Evaluate the PDF of the DISTRIBUTION at X.  X must be a pointer to a
     double array of appropriate size (i.e. of the same size as given to the
     `unur_distr_cvec_new' call) that contains the vector for which the
     function has to be evaluated.

     Notice that DISTRIBUTION must not be the `NULL' pointer.  If the
     corresponding function is not available for the DISTRIBUTION,
     `UNUR_INFINITY' is returned and `unur_errno' is set to
     `UNUR_ERR_DISTR_DATA'.

 -- Function: int unur_distr_cvec_eval_dpdf (double* RESULT, const DOUBLE* X,
          UNUR_DISTR* DISTRIBUTION)
     Evaluate the gradient of the PDF of the DISTRIBUTION at X.  The result is
     stored in the double array RESULT.  Both RESULT and X must be pointer to
     double arrays of appropriate size (i.e. of the same size as given to the
     `unur_distr_cvec_new' call).

     Notice that DISTRIBUTION must not be the `NULL' pointer.  If the
     corresponding function is not available for the DISTRIBUTION, an error
     code is returned and `unur_errno' is set to `UNUR_ERR_DISTR_DATA' (RESULT
     is left unmodified).

 -- Function: double unur_distr_cvec_eval_pdpdf (const DOUBLE* X, int COORD,
          UNUR_DISTR* DISTRIBUTION)
     Evaluate the partial derivative of the PDF of the DISTRIBUTION at X for
     the coordinate COORD.  X must be a pointer to a double array of
     appropriate size (i.e. of the same size as given to the
     `unur_distr_cvec_new' call) that contains the vector for which the
     function has to be evaluated.

     Notice that COORD must be an integer from {0,...,dim-1}.

     Notice that DISTRIBUTION must not be the `NULL' pointer.  If the
     corresponding function is not available for the DISTRIBUTION,
     `UNUR_INFINITY' is returned and `unur_errno' is set to
     `UNUR_ERR_DISTR_DATA'.

 -- Function: int unur_distr_cvec_set_logpdf (UNUR_DISTR* DISTRIBUTION,
          UNUR_FUNCT_CVEC* LOGPDF)
 -- Function: int unur_distr_cvec_set_dlogpdf (UNUR_DISTR* DISTRIBUTION,
          UNUR_VFUNCT_CVEC* DLOGPDF)
 -- Function: int unur_distr_cvec_set_pdlogpdf (UNUR_DISTR* DISTRIBUTION,
          UNUR_FUNCTD_CVEC* PDLOGPDF)
 -- Function: UNUR_FUNCT_CVEC* unur_distr_cvec_get_logpdf (const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: UNUR_VFUNCT_CVEC* unur_distr_cvec_get_dlogpdf (const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: double unur_distr_cvec_eval_logpdf (const DOUBLE* X, UNUR_DISTR*
          DISTRIBUTION)
 -- Function: int unur_distr_cvec_eval_dlogpdf (double* RESULT, const DOUBLE*
          X, UNUR_DISTR* DISTRIBUTION)
 -- Function: double unur_distr_cvec_eval_pdlogpdf (const DOUBLE* X, int COORD,
          UNUR_DISTR* DISTRIBUTION)
     Analogous calls for the logarithm of the density function.

 -- Function: int unur_distr_cvec_set_mean (UNUR_DISTR* DISTRIBUTION, const
          DOUBLE* MEAN)
     Set mean vector for multivariate DISTRIBUTION.  MEAN must be a pointer to
     an array of size `dim', where `dim' is the dimension returned by
     `unur_distr_get_dim'.  A `NULL' pointer for MEAN is interpreted as the zero
     vector (0,...,0).

     *Important:* If the parameters of a distribution from the UNU.RAN library
     of standard distributions (*note Standard distributions: Stddist.)  are
     changed, then neither its mode nor the normalization constant are updated.
     Please use the respective calls `unur_distr_cvec_upd_mode' and
     `unur_distr_cvec_upd_pdfvol'.

 -- Function: const double* unur_distr_cvec_get_mean (const UNUR_DISTR*
          DISTRIBUTION)
     Get the mean vector of the DISTRIBUTION. The function returns a pointer to
     an array of size `dim'.  If the mean vector is not marked as known the
     `NULL' pointer is returned and `unur_errno' is set to `UNUR_ERR_DISTR_GET'.

     _Important:_ Do *not* modify the array that holds the mean vector!

 -- Function: int unur_distr_cvec_set_covar (UNUR_DISTR* DISTRIBUTION, const
          DOUBLE* COVAR)
     Set covariance matrix for multivariate DISTRIBUTION.  COVAR must be a
     pointer to an array of size `dim' x `dim', where `dim' is the dimension
     returned by `unur_distr_get_dim'.  The rows of the matrix have to be stored
     consecutively in this array.

     COVAR must be a variance-covariance matrix of the DISTRIBUTION, i.e. it
     must be symmetric and positive definit and its diagonal entries (i.e. the
     variance of the components of the random vector) must be strictly positive.
     The Cholesky factor is computed (and stored) to verify the positive
     definiteness condition.  Notice that the inverse of the given covariance
     matrix is automatically computed when it is requested by some routine.
     Notice that the computation of this inverse matrix is unstable in case of
     high correlations and/or high dimensions. Thus it might fail and methods
     that require this inverse cannot be used.  As an alternative the inverse
     of the covariance matrix can be directly set by a
     `unur_distr_cvec_set_covar_inv' call.

     A `NULL' pointer for COVAR is interpreted as the identity matrix.

     _Important:_ This entry is abused in some methods which do not require the
     covariance matrix. It is then used to perform some transformation to
     obtain better performance.

     _Important:_ In case of an error (e.g. because COVAR is not a valid
     covariance matrix) an error code is returned.  Moreover, the covariance
     matrix is not set and is marked as unknown. A previously set covariance
     matrix is then no longer available.

     *Important:* If the parameters of a distribution from the UNU.RAN library
     of standard distributions (*note Standard distributions: Stddist.)  are
     changed, then neither its mode nor the normalization constant are updated.
     Please use the respective calls `unur_distr_cvec_upd_mode' and
     `unur_distr_cvec_upd_pdfvol'.  _Remark:_ UNU.RAN does not check whether
     the an eventually set covariance matrix and a rank-correlation matrix do
     not contradict each other.

 -- Function: int unur_distr_cvec_set_covar_inv (UNUR_DISTR* DISTRIBUTION,
          const DOUBLE* COVAR_INV)
     Set inverse of the covariance matrix for multivariate DISTRIBUTION.
     COVAR_INV must be a pointer to an array of size `dim' x `dim', where `dim'
     is the dimension returned by `unur_distr_get_dim'.  The rows of the matrix
     have to be stored consecutively in this array.

     COVAR_INV must be symmetric and positive definit. Only the symmetry of the
     matrix is checked.

     A `NULL' pointer for COVAR_INV is interpreted as the identity matrix.

     _Important:_ In case of an error (because COVAR_INV is not symetric) an
     error code is returned.  Moreover, the inverse of the covariance matrix is
     not set and is marked as unknown. A previously set inverse matrix is then
     no longer available.

     _Remark:_ UNU.RAN does not check whether the given matrix is positive
     definit.

     _Remark:_ UNU.RAN does not check whether the matrix COVAR_INV is the
     inverse of the eventually set covariance matrix.

 -- Function: const double* unur_distr_cvec_get_covar (const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: const double* unur_distr_cvec_get_cholesky (const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: const double* unur_distr_cvec_get_covar_inv (UNUR_DISTR*
          DISTRIBUTION)
     Get covariance matrix of DISTRIBUTION, its Cholesky factor, and its
     inverse, respectively. The function returns a pointer to an array of size
     `dim' x `dim'.  The rows of the matrix are stored consecutively in this
     array.  If the requested matrix is not marked as known the `NULL' pointer
     is returned and `unur_errno' is set to `UNUR_ERR_DISTR_GET'.

     _Important:_ Do *not* modify the array that holds the covariance matrix!

     _Remark:_ The inverse of the covariance matrix is computed if it is not
     already stored.

 -- Function: int unur_distr_cvec_set_rankcorr (UNUR_DISTR* DISTRIBUTION, const
          DOUBLE* RANKCORR)
     Set rank-correlation matrix (Spearman's correlation) for multivariate
     DISTRIBUTION.  RANKCORR must be a pointer to an array of size `dim' x
     `dim', where `dim' is the dimension returned by `unur_distr_get_dim'.  The
     rows of the matrix have to be stored consecutively in this array.

     RANKCORR must be a rank-correlation matrix of the DISTRIBUTION, i.e. it
     must be symmetric and positive definite and its diagonal entries must be
     equal to `1'.

     The Cholesky factor is computed (and stored) to verify the positive
     definiteness condition.

     A `NULL' pointer for RANKCORR is interpreted as the identity matrix.

     _Important:_ In case of an error (e.g. because RANKCORR is not a valid
     rank-correlation matrix) an error code is returned.  Moreover, the
     rank-correlation matrix is not set and is marked as unknown. A previously
     set rank-correlation matrix is then no longer available.

     _Remark:_ UNU.RAN does not check whether the an eventually set covariance
     matrix and a rank-correlation matrix do not contradict each other.

 -- Function: const double* unur_distr_cvec_get_rankcorr (const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: const double* unur_distr_cvec_get_rk_cholesky (const UNUR_DISTR*
          DISTRIBUTION)
     Get rank-correlation matrix and its cholesky factor, respectively, of
     DISTRIBUTION. The function returns a pointer to an array of size `dim' x
     `dim'.  The rows of the matrix are stored consecutively in this array.  If
     the requested matrix is not marked as known the `NULL' pointer is returned
     and `unur_errno' is set to `UNUR_ERR_DISTR_GET'.

     _Important:_ Do *not* modify the array that holds the rank-correlation
     matrix!

 -- Function: int unur_distr_cvec_set_marginals (UNUR_DISTR* DISTRIBUTION,
          UNUR_DISTR* MARGINAL)
     Sets marginal distributions of the given DISTRIBUTION to the same MARGINAL
     distribution object. The MARGINAL distribution must be an instance of a
     continuous univariate distribution object. Notice that the marginal
     distribution is copied into the DISTRIBUTION object.

 -- Function: int unur_distr_cvec_set_marginal_array (UNUR_DISTR* DISTRIBUTION,
          UNUR_DISTR** MARGINALS)
     Analogously to the above `unur_distr_cvec_set_marginals' call.  However,
     now an array MARGINALS of the pointers to each of the marginal
     distributions must be given. It *must* be an array of size `dim', where
     `dim' is the dimension returned by `unur_distr_get_dim'.  _Notice_: Local
     copies for each of the entries are stored in the DISTRIBUTION object. If
     some of these entries are identical (i.e. contain the same pointer), then
     for each of these a new copy is made.

 -- Function: int unur_distr_cvec_set_marginal_list (UNUR_DISTR* DISTRIBUTION,
          ...)
     Similar to the above `unur_distr_cvec_set_marginal_array' call.  However,
     now the pointers to the particular marginal distributions can be given as
     parameter and does not require an array of pointers. Additionally the
     given distribution objects are immediately destroyed. Thus calls like
     `unur_distr_normal' can be used as arguments.  (With
     `unur_distr_cvec_set_marginal_array' the result of such call has to be
     stored in a pointer since it has to be freed afterwarts to avoid memory
     leaks!)

     The number of pointers to in the list of function arguments *must* be
     equal to the dimension of the DISTRIBUTION, i.e. the dimension returned by
     `unur_distr_get_dim'.  If one of the given pointer to marginal
     distributions is the `NULL' pointer then the marginal distributions of
     DISTRIBUTION are not set (or previous settings are not changed) and an
     error code is returned.

     *Important:* All distribution objects given in the argument list are
     destroyed!

 -- Function: const UNUR_DISTR* unur_distr_cvec_get_marginal (const UNUR_DISTR*
          DISTRIBUTION, int N)
     Get pointer to the N-th marginal distribution object from the given
     multivariate DISTRIBUTION.  If this does not exist, `NULL' is returned.
     The marginal distributions are enumerated from `1' to `dim', where `dim'
     is the dimension returned by `unur_distr_get_dim'.

 -- Function: int unur_distr_cvec_set_pdfparams (UNUR_DISTR* DISTRIBUTION,
          const DOUBLE* PARAMS, int N_PARAMS)
     Sets array of parameters for DISTRIBUTION. There is an upper limit for the
     number of parameters `n_params'. It is given by the macro
     `UNUR_DISTR_MAXPARAMS' in `unuran_config.h'. (It is set to 5 by default
     but can be changed to any appropriate nonnegative number.)  If N_PARAMS is
     negative or exceeds this limit no parameters are copied into the
     distribution object and `unur_errno' is set to `UNUR_ERR_DISTR_NPARAMS'.

     For standard distributions from the UNU.RAN library the parameters are
     checked. Moreover, the domain is updated automatically.  If the given
     parameters are invalid for the standard distribution, then no parameters
     are set and an error code is returned.  Notice that the given parameter
     list for such a distribution is handled in the same way as in the
     corresponding `new' calls, i.e. optional parameters for the PDF that are
     not present in the given list are (re-)set to their default values.

     *Important:* If the parameters of a distribution from the UNU.RAN library
     of standard distributions (*note Standard distributions: Stddist.)  are
     changed, then neither its mode nor the normalization constant are updated.
     Please use the respective calls `unur_distr_cvec_upd_mode' and
     `unur_distr_cvec_upd_pdfvol'.

 -- Function: int unur_distr_cvec_get_pdfparams (const UNUR_DISTR*
          DISTRIBUTION, const DOUBLE** PARAMS)
     Get number of parameters of the PDF and set pointer PARAMS to array of
     parameters. If no parameters are stored in the object, an error code is
     returned and `params' is set to `NULL'.

     _Important:_ Do *not* change the entries in PARAMS!

 -- Function: int unur_distr_cvec_set_pdfparams_vec (UNUR_DISTR* DISTRIBUTION,
          int PAR, const DOUBLE* PARAM_VEC, int N_PARAMS)
     This function provides an interface for additional vector parameters for a
     multivariate DISTRIBUTION besides mean vector and covariance matrix which
     have their own calls.

     It sets the parameter with number PAR.  PAR indicates directly which of
     the parameters is set and must be a number between `0' and
     `UNUR_DISTR_MAXPARAMS'-1 (the upper limit of possible parameters defined in
     `unuran_config.h'; it is set to 5 but can be changed to any appropriate
     nonnegative number.)

     The entries of a this parameter are given by the array PARAM_VEC of size
     N_PARAMS. Notice that using this interface an An (n x m)-matrix has to be
     stored in an array of length N_PARAMS = n times m; where the rows of the
     matrix are stored consecutively in this array.

     Due to great variety of possible parameters for a multivariate
     DISTRIBUTION there is no simpler interface.

     If PARAM_VEC is `NULL' then the corresponding entry is cleared.

     *Important:* If the parameters of a distribution from the UNU.RAN library
     of standard distributions (*note Standard distributions: Stddist.)  are
     changed, then neither its mode nor the normalization constant are updated.
     Please use the respective calls `unur_distr_cvec_upd_mode' and
     `unur_distr_cvec_upd_pdfvol'.  If an error occurs no parameters are copied
     into the parameter object `unur_errno' is set to `UNUR_ERR_DISTR_DATA'.

 -- Function: int unur_distr_cvec_get_pdfparams_vec (const UNUR_DISTR*
          DISTRIBUTION, int PAR, const DOUBLE** PARAM_VECS)
     Get parameter of the PDF with number PAR.  The pointer to the parameter
     array is stored in PARAM_VECS, its size is returned by the function.  If
     the requested parameter is not set, then an error code is returned and
     `params' is set to `NULL'.

     _Important:_ Do *not* change the entries in PARAM_VECS!

 -- Function: int unur_distr_cvec_set_domain_rect (UNUR_DISTR* DISTRIBUTION,
          const DOUBLE* LOWERLEFT, const DOUBLE* UPPERRIGHT)
     Set rectangular domain for DISTRIBUTION with LOWERLEFT and UPPERRIGHT
     vertices. Both must be pointer to an array of the size returned by
     `unur_distr_get_dim'.  A `NULL' pointer is interpreted as the zero vector
     (0,...,0).  For setting a coordinate of the boundary to +/- infinity use
     `+/- UNUR_INFINITY'.  The LOWERLEFT vertex must be strictly smaller than
     UPPERRIGHT in each component. Otherwise no domain is set and `unur_errno'
     is set to `UNUR_ERR_DISTR_SET'.

     By default the domain of a distribution is unbounded. Thus one can use
     this call to truncate an existing distribution.

     _Important:_ Changing the domain of DISTRIBUTION marks derived parameters
     like the mode or the center as unknown and must be set _after_ changing
     the domain. This is important for the already set (or default) value for
     the center does not fall into the given domain.  Notice that calls of the
     PDF and derived functions return `0.' when the parameter is not contained
     in the domain.

 -- Function: int unur_distr_cvec_is_indomain (const DOUBLE* X, const
          UNUR_DISTR* DISTRIBUTION)
     Check whether X falls into the domain of DISTRIBUTION.

Derived parameters
..................

The following paramters *must* be set whenever one of the essential parameters
has been set or changed (and the parameter is required for the chosen method).

 -- Function: int unur_distr_cvec_set_mode (UNUR_DISTR* DISTRIBUTION, const
          DOUBLE* MODE)
     Set mode of the DISTRIBUTION. MODE must be a pointer to an array of the
     size returned by `unur_distr_get_dim'.  A `NULL' pointer for MODE is
     interpreted as the zero vector (0,...,0).

 -- Function: int unur_distr_cvec_upd_mode (UNUR_DISTR* DISTRIBUTION)
     Recompute the mode of the DISTRIBUTION. This call works properly for
     distribution objects from the UNU.RAN library of standard distributions
     when the corresponding function is available. If it failes `unur_errno' is
     set to `UNUR_ERR_DISTR_DATA'.

 -- Function: const double* unur_distr_cvec_get_mode (UNUR_DISTR* DISTRIBUTION)
     Get mode of the DISTRIBUTION. The function returns a pointer to an array
     of the size returned by `unur_distr_get_dim'.  If the mode is not marked
     as known the `NULL' pointer is returned and `unur_errno' is set to
     `UNUR_ERR_DISTR_GET'.  (There is no difference between the case where no
     routine for computing the mode is available and the case where no mode
     exists for the DISTRIBUTION at all.)

     _Important:_ Do *not* modify the array that holds the mode!

 -- Function: int unur_distr_cvec_set_center (UNUR_DISTR* DISTRIBUTION, const
          DOUBLE* CENTER)
     Set center of the DISTRIBUTION. CENTER must be a pointer to an array of
     the size returned by `unur_distr_get_dim'.  A `NULL' pointer for CENTER is
     interpreted as the zero vector (0,...,0).

     The center is used by some methods to shift the distribution in order to
     decrease numerical round-off error.  If not given explicitly a default is
     used.  Moreover, it is used as starting point for several numerical search
     algorithm (e.g. for the mode). Then CENTER must be a pointer where the
     call to the PDF returns a non-zero value.  In particular CENTER must
     contained in the domain of the distribution.

     Default: The mode, if given by a `unur_distr_cvec_set_mode' call; else the
     mean, if given by a `unur_distr_cvec_set_mean' call; otherwise the null
     vector (0,...,0).

 -- Function: const double* unur_distr_cvec_get_center (UNUR_DISTR*
          DISTRIBUTION)
     Get center of the DISTRIBUTION. The function returns a pointer to an array
     of the size returned by `unur_distr_get_dim'.  It always returns some
     point as there always exists a default for the center, see
     `unur_distr_cvec_set_center'.  _Important:_ Do *not* modify the array that
     holds the center!

 -- Function: int unur_distr_cvec_set_pdfvol (UNUR_DISTR* DISTRIBUTION, double
          VOLUME)
     Set the volume below the PDF. If VOL is non-positive, no volume is set and
     `unur_errno' is set to `UNUR_ERR_DISTR_SET'.

 -- Function: int unur_distr_cvec_upd_pdfvol (UNUR_DISTR* DISTRIBUTION)
     Recompute the volume below the PDF of the distribution.  It only works for
     distribution objects from the UNU.RAN library of standard distributions
     when the corresponding function is available. Otherwise `unur_errno' is
     set to `UNUR_ERR_DISTR_DATA'.

     This call also sets the normalization constant such that the given PDF is
     the derivative of a given CDF, i.e. the volume is 1.

 -- Function: double unur_distr_cvec_get_pdfvol (UNUR_DISTR* DISTRIBUTION)
     Get the volume below the PDF of the DISTRIBUTION. If this volume is not
     known,
     `unur_distr_cont_upd_pdfarea' is called to compute it. If this is not
     successful `UNUR_INFINITY' is returned and `unur_errno' is set to
     `UNUR_ERR_DISTR_GET'.

4.6 Continuous univariate full conditional distribution
=======================================================

Full conditional distribution for a given continuous multivariate
distributiion. The condition is a position vector and either a variable that is
variated or a vector that indicates the direction on which the random vector
can variate.

   There is a subtle difference between using direction vector and using the
K-th variable.  When a direction vector is given the PDF of the conditional
distribution is defined by f(t) = PDF(pos + t * dir).  When a variable is
selected the full conditional distribution with all other variables fixed is
used.

   This is a special case of a continuous univariate distribution and thus they
have most of these parameters (with the exception that functions cannot be
changed). Additionally,

   - there is a call to extract the underlying multivariate distribution,

   - and a call to handle the variables that are fixed and the direction for
     changing the random vector.


   This distibution type is primarily used for evaluation the conditional
distribution and its derivative (as required for, e.g., the Gibbs sampler). The
density is not normalized (i.e. does not integrate to one). Mode and area are
not available and it does not make sense to use any call to set or change
parameters except the ones given below.

Function reference
------------------

 -- Function: UNUR_DISTR* unur_distr_condi_new (const UNUR_DISTR* DISTRIBUTION,
          const DOUBLE* POS, const DOUBLE* DIR, int K)
     Create an object for full conditional distribution for the given
     DISTRIBUTION. The condition is given by a position vector POS and either
     the K-th variable that is variated or the vector DIR that contains the
     direction on which the random vector can variate.

     DISTRIBUTION must be a pointer to a multivariate continuous distribution.
     POS must be a pointer to an array of size `dim', where `dim' is the
     dimension of the underlying distribution object.  DIR must be a pointer to
     an array if size `dim' or `NULL'.  K must be in the range `0, ..., dim-1'.
     If the K-th variable is used, DIR must be set to `NULL'.

     _Notice:_ There is a subtle difference between using direction vector DIR
     and using the K-th variable.  When DIR is given, the current position POS
     is mapped into 0 of the conditional distribution and the derivative is
     taken from the function PDF(POS+t*DIR) w.r.t. t.  On the other hand, when
     the coordinate K is used (i.e., when DIR is set to `NULL'), the full
     conditional distribution of the distribution is considered (as used for
     the Gibbs sampler).  In particular, the current point is just projected
     into the one-dimensional subspace without mapping it into the point 0.

     _Notice:_ If a coordinate K is used, then the K-th partial derivative is
     used if it as available. Otherwise the gradient is computed and the K-th
     component is returned.

     The resulting generator object is of the same type as of a
     `unur_distr_cont_new' call.

 -- Function: int unur_distr_condi_set_condition (struct UNUR_DISTR*
          DISTRIBUTION, const DOUBLE* POS, const DOUBLE* DIR, int K)
     Set/change condition for conditional DISTRIBUTION.  Change values of fixed
     variables to POS and use direction DIR or K-th variable of conditional
     DISTRIBUTION.

     POS must be a pointer to an array of size `dim', where `dim' is the
     dimension of the underlying distribution object.  DIR must be a pointer to
     an array if size `dim' or `NULL'.  K must be in the range `0, ..., dim-1'.
     If the K-th variable is used, DIR must be set to `NULL'.

     _Notice:_ There is a subtle difference between using direction vector DIR
     and using the K-th variable.  When DIR is given, the current position POS
     is mapped into 0 of the conditional distribution and the derivative is
     taken from the function PDF(POS+t*DIR) w.r.t. t.  On the other hand, when
     the coordinate K is used (i.e., when DIR is set to `NULL'), the full
     conditional distribution of the distribution is considered (as used for
     the Gibbs sampler).  In particular, the current point is just projected
     into the one-dimensional subspace without mapping it into the point 0.

 -- Function: int unur_distr_condi_get_condition (struct UNUR_DISTR*
          DISTRIBUTION, const DOUBLE** POS, const DOUBLE** DIR, int* K)
     Get condition for conditional DISTRIBUTION.  The values for the fixed
     variables are stored in POS, which must be a pointer to an array of size
     `dim'.  The condition is stored in DIR and K, respectively.

     _Important:_ Do *not* change the entries in POS and DIR!

 -- Function: const UNUR_DISTR* unur_distr_condi_get_distribution (const
          UNUR_DISTR* DISTRIBUTION)
     Get pointer to distribution object for underlying distribution.

4.7 Continuous empirical multivariate distributions
===================================================

Empirical multivariate distributions are just lists of vectors (with the same
dimension).  Thus there are only calls to insert these data.  How these data
are used to sample from the empirical distribution depends from the chosen
generation method.

Function reference
------------------

 -- Function: UNUR_DISTR* unur_distr_cvemp_new (int DIM)
     Create a new (empty) object for an empirical multivariate continuous
     distribution. DIM is the number of components of the random vector (i.e.
     its dimension). It must be at least 2; otherwise `unur_distr_cemp_new'
     should be used to create an object for an empirical univariate
     distribution.

Essential parameters
....................

 -- Function: int unur_distr_cvemp_set_data (UNUR_DISTR* DISTRIBUTION, const
          DOUBLE* SAMPLE, int N_SAMPLE)
     Set observed sample for empirical DISTRIBUTION.  SAMPLE is an array of
     doubles of size `dim' x N_SAMPLE, where `dim' is the dimension of the
     DISTRIBUTION returned by `unur_distr_get_dim'.  The data points must be
     stored consecutively in SAMPLE, i.e., data points (x1, y1), (x2, y2), ...
     are given as an array {x1, y1, x2, y2, ...}.

 -- Function: int unur_distr_cvemp_read_data (UNUR_DISTR* DISTRIBUTION, const
          CHAR* FILENAME)
     Read data from file `filename'.  It reads the first `dim' numbers from
     each line, where `dim' is the dimension of the DISTRIBUTION returned by
     `unur_distr_get_dim'.  Numbers are parsed by means of the C standard
     routine `strtod'.  Lines that do not start with `+', `-', `.', or a digit
     are ignored. (Beware of lines starting with a blank!)

     In case of an error (file cannot be opened, too few entries in a line,
     invalid string for double in line) no data are copied into the
     distribution object and an error code is returned.

 -- Function: int unur_distr_cvemp_get_data (const UNUR_DISTR* DISTRIBUTION,
          const DOUBLE** SAMPLE)
     Get number of samples and set pointer SAMPLE to array of observations. If
     no sample has been given, an error code is returned and SAMPLE is set to
     `NULL'.  If successful SAMPLE points to an array of length `dim' x
     `n_sample', where `dim' is the dimension of the distribution returned by
     `unur_distr_get_dim' and `n_sample' the return value of the function.

     _Important:_ Do *not* modify the array SAMPLE.

4.8 MATRix distributions
========================

Distributions for random matrices. Notice that UNU.RAN uses arrays of `double's
to handle matrices. The rows of the matrix are stored consecutively.

Function reference
------------------

 -- Function: UNUR_DISTR* unur_distr_matr_new (int N_ROWS, int N_COLS)
     Create a new (empty) object for a matrix distribution. N_ROWS and N_COLS
     are the respective numbers of rows and columns of the random matrix (i.e.
     its dimensions). It is also possible to have only one number or rows
     and/or columns.  Notice, however, that this is treated as a distribution
     of random matrices with only one row or column or component and not as a
     distribution of vectors or real numbers. For the latter
     `unur_distr_cont_new' or `unur_distr_cvec_new' should be used to create an
     object for a univariate distribution and a multivariate (vector)
     distribution, respectively.

Essential parameters
....................

 -- Function: int unur_distr_matr_get_dim (const UNUR_DISTR* DISTRIBUTION, int*
          N_ROWS, int* N_COLS)
     Get number of rows and columns of random matrix (its dimension).  It
     returns the total number of components. If successfull `UNUR_SUCCESS' is
     returned.

4.9 Discrete univariate distributions
=====================================

The calls in this section can be applied to discrete univariate distributions.

   - Create a `new' instance of a discrete univariate distribution.

   - Handle and evaluate distribution function (CDF, `cdf') and probability
     mass function (PMF, `pmf').  The following is important:
        . `pmf' need not be normalized, i.e., any summable nonnegative function
          on the set of intergers can be used.

        . `cdf' must be a distribution function, i.e. it must be monotonically
          increasing with range [0,1].

        . If `cdf' and `pdf' are used together for a pariticular generation
          method, then `pmf' must be normalized, i.e. it must sum to 1.

   - Alternatively, `cdf' and `pdf' can be provided as `str'ings instead of
     function pointers.

   - Some generation methods require a (finite) probability vector (PV, `pv'),
     i.e. an array of `double's.  It can be automatically computed if the `pmf'
     is given but `pv' is not.

   - Set (and change) parameters (`pmfparams') and the total sum (`pmfsum') of
     the given PMF or PV.

   - Set the `mode' of the distribution.

   - Set the `domain' of the distribution.


Function reference
------------------

 -- Function: UNUR_DISTR* unur_distr_discr_new (void)
     Create a new (empty) object for a univariate discrete distribution.

Essential parameters
....................

There are two interfaces for discrete univariate distributions: Either provide
a (finite) probability vector (PV).  Or provide a probability mass function
(PMF). For the latter case there are also a couple of derived parameters that
are not required when a PV is given.

   It is not possible to set both a PMF and a PV directly. However, the PV can
be computed from the PMF (or the CDF if no PMF is available) by means of a
`unur_distr_discr_make_pv' call.  If both the PV and the PMF are given in the
distribution object it depends on the generation method which of these is used.

 -- Function: int unur_distr_discr_set_pv (UNUR_DISTR* DISTRIBUTION, const
          DOUBLE* PV, int N_PV)
     Set finite probability vector (PV) for the DISTRIBUTION. It is not
     necessary that the entries in the given PV sum to 1.  N_PV must be
     positive. However, there is no testing whether all entries in PV are
     non-negative.

     If no domain has been set, then the left boundary is set to `0', by
     default. If N_PV is too large, e.g. because left boundary + N_PV exceeds
     the range of integers, then the call fails.

     Notice that it is not possible to set both a PV and a PMF or CDF.  If the
     PMF or CDF is set first one cannot set the PV.  If the PMF or CDF is set
     first after a PV is set, the latter is removed (and recomputed using
     `unur_distr_discr_make_pv' when required).

 -- Function: int unur_distr_discr_make_pv (UNUR_DISTR* DISTRIBUTION)
     Compute a PV when a PMF or CDF is given. However, when the domain is not
     given or is too large and the sum over the PMF is given then the (right)
     tail of the DISTRIBUTION is chopped off such that the probability for the
     tail region is less than 1.e-8.  If the sum over the PMF is not given a PV
     of maximal length is computed.

     The maximal size of the created PV is bounded by the macro
     `UNUR_MAX_AUTO_PV' that is defined in `unuran_config.h'.

     If successful, the length of the generated PV is returned.  If the sum
     over the PMF on the chopped tail is not neglible small (i.e. greater than
     1.e-8 or unknown) than the negative of the length of the PV is returned and
     `unur_errno' is set to `UNUR_ERR_DISTR_SET'.

     Notice that the left boundary of the PV is set to `0' by default when a
     discrete distribution object is created from scratch.

     If computing a PV fails for some reasons, an error code is returned and
     `unur_errno' is set to `UNUR_ERR_DISTR_SET'.

 -- Function: int unur_distr_discr_get_pv (const UNUR_DISTR* DISTRIBUTION,
          const DOUBLE** PV)
     Get length of PV of the DISTRIBUTION and set pointer PV to array of
     probabilities. If no PV is given, an error code is returned and PV is set
     to `NULL'.
     (It does not call `unur_distr_discr_make_pv' !)

 -- Function: int unur_distr_discr_set_pmf (UNUR_DISTR* DISTRIBUTION,
          UNUR_FUNCT_DISCR* PMF)
 -- Function: int unur_distr_discr_set_cdf (UNUR_DISTR* DISTRIBUTION,
          UNUR_FUNCT_DISCR* CDF)
     Set respective pointer to the PMF and the CDF of the DISTRIBUTION.  These
     functions must be of type `double funct(int k, const UNUR_DISTR *distr)'.

     It is important to note that all these functions must return a result for
     all integers K. E.g., if the domain of a given PMF is the interval
     {1,2,3,...,100}, than the given function must return `0.0' for all points
     outside this interval.

     The default domain for the PMF or CDF is [`0', `INT_MAX'].  The domain can
     be changed using a `unur_distr_discr_set_domain' call.

     It is not possible to change such a function. Once the PMF or CDF is set
     it cannot be overwritten. A new distribution object has to be used instead.

     Notice that it is not possible to set both a PV and a PMF or CDF.  If the
     PMF or CDF is set first one cannot set the PV.  If the PMF or CDF is set
     first after a PV is set, the latter is removed (and recomputed using
     `unur_distr_discr_make_pv' when required).

 -- Function: int unur_distr_discr_set_invcdf (UNUR_DISTR* DISTRIBUTION,
          UNUR_IFUNCT_DISCR* INVCDF)
     Set inverse CDF of the DISTRIBUTION.  INVCDF must be a pointer must be of
     type `int funct(double x, const UNUR_DISTR *distr)', i.e., it should
     return a `double'.

 -- Function: double unur_distr_discr_eval_pv (int K, const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: double unur_distr_discr_eval_pmf (int K, const UNUR_DISTR*
          DISTRIBUTION)
 -- Function: double unur_distr_discr_eval_cdf (int K, const UNUR_DISTR*
          DISTRIBUTION)
     Evaluate the PV, PMF, and the CDF, respectively, at k.  Notice that
     DISTRIBUTION must not be the `NULL' pointer.  If no PV is set for the
     DISTRIBUTION, then `unur_distr_discr_eval_pv' behaves like
     `unur_distr_discr_eval_pmf'.  If the corresponding function is not
     available for the DISTRIBUTION, `UNUR_INFINITY' is returned and
     `unur_errno' is set to `UNUR_ERR_DISTR_DATA'.

     _IMPORTANT:_ In the case of a truncated standard distribution these calls
     always return the respective values of the _untruncated_ distribution!

 -- Function: int unur_distr_discr_eval_invcdf (double U, const UNUR_DISTR*
          DISTRIBUTION)
     Evaluate the inverse CDF at U.  Notice that DISTRIBUTION must not be the
     `NULL' pointer.  If the corresponding function is not available for the
     distribution, `INT_MAX' is returned and `unur_errno' is set to
     `UNUR_ERR_DISTR_DATA'.

     _IMPORTANT:_ In the case of a truncated standard distribution these calls
     always return the respective values of the _untruncated_ distribution!

 -- Function: int unur_distr_discr_set_pmfstr (UNUR_DISTR* DISTRIBUTION, const
          CHAR* PMFSTR)
     This function provides an alternative way to set a PMF of the DISTRIBUTION.
     PMFSTR is a character string that contains the formula for the PMF, see
     *note Function String: StringFunct, for details.  See also the remarks for
     the `unur_distr_discr_set_pmf' call.

     It is not possible to call this funtion twice or to call this function
     after a `unur_distr_discr_set_pmf' call.

 -- Function: int unur_distr_discr_set_cdfstr (UNUR_DISTR* DISTRIBUTION, const
          CHAR* CDFSTR)
     This function provides an alternative way to set a CDF; analogously to the
     `unur_distr_discr_set_pmfstr' call.

 -- Function: char* unur_distr_discr_get_pmfstr (const UNUR_DISTR* DISTRIBUTION)
 -- Function: char* unur_distr_discr_get_cdfstr (const UNUR_DISTR* DISTRIBUTION)
     Get pointer to respective string for PMF and CDF of DISTRIBUTION that is
     given via the string interface.  This call allocates memory to produce
     this string. It should be freed when it is not used any more.

 -- Function: int unur_distr_discr_set_pmfparams (UNUR_DISTR* DISTRIBUTION,
          const DOUBLE* PARAMS, int N_PARAMS)
     Set array of parameters for DISTRIBUTION. There is an upper limit for the
     number of parameters N_PARAMS. It is given by the macro
     `UNUR_DISTR_MAXPARAMS' in `unuran_config.h'. (It is set to 5 but can be
     changed to any appropriate nonnegative number.)  If N_PARAMS is negative
     or exceeds this limit no parameters are copied into the DISTRIBUTION
     object and `unur_errno' is set to `UNUR_ERR_DISTR_NPARAMS'.

     For standard distributions from the UNU.RAN library the parameters are
     checked. Moreover, the domain is updated automatically unless it has been
     changed before by a `unur_distr_discr_set_domain' call.  If the given
     parameters are invalid for the standard distribution, then no parameters
     are set and an error code is returned.  Notice that the given parameter
     list for such a distribution is handled in the same way as in the
     corresponding `new' calls, i.e. optional parameters for the PDF that are
     not present in the given list are (re-)set to their default values.

     _Important:_ Integer parameter must be given as `double's.

 -- Function: int unur_distr_discr_get_pmfparams (const UNUR_DISTR*
          DISTRIBUTION, const DOUBLE** PARAMS)
     Get number of parameters of the PMF and set pointer PARAMS to array of
     parameters. If no parameters are stored in the object, an error code is
     returned and `params' is set to `NULL'.

 -- Function: int unur_distr_discr_set_domain (UNUR_DISTR* DISTRIBUTION, int
          LEFT, int RIGHT)
     Set the left and right borders of the domain of the DISTRIBUTION. This can
     also be used to truncate an existing distribution. For setting the
     boundary to +/- infinity use `INT_MIN' and `INT_MAX', respectively.  If
     RIGHT is not strictly greater than LEFT no domain is set and `unur_errno'
     is set to `UNUR_ERR_DISTR_SET'.  It is allowed to use this call to
     increase the domain.  If the PV of the discrete distribution is used, than
     the right boudary is ignored (and internally set to LEFT + size of PV - 1).
     Notice that `INT_MIN' and `INT_MAX' are interpreted as (minus/plus)
     infinity.

     Default: [`0', `INT_MAX'].

 -- Function: int unur_distr_discr_get_domain (const UNUR_DISTR* DISTRIBUTION,
          int* LEFT, int* RIGHT)
     Get the left and right borders of the domain of the DISTRIBUTION. If the
     domain is not set explicitly the interval [`INT_MIN', `INT_MAX'] is
     assumed and returned.  When a PV is given then the domain is set
     automatically to [`0',size of PV - 1].

Derived parameters
..................

The following paramters *must* be set whenever one of the essential parameters
has been set or changed (and the parameter is required for the chosen method).

 -- Function: int unur_distr_discr_set_mode (UNUR_DISTR* DISTRIBUTION, int MODE)
     Set mode of DISTRIBUTION.

 -- Function: int unur_distr_discr_upd_mode (UNUR_DISTR* DISTRIBUTION)
     Recompute the mode of the DISTRIBUTION. This call works properly for
     distribution objects from the UNU.RAN library of standard distributions
     when the corresponding function is available.  Otherwise a (slow)
     numerical mode finder is used. It only works properly for unimodal
     probability mass functions. If it failes `unur_errno' is set to
     `UNUR_ERR_DISTR_DATA'.

 -- Function: int unur_distr_discr_get_mode (UNUR_DISTR* DISTRIBUTION)
     Get mode of DISTRIBUTION. If the mode is not marked as known,
     `unur_distr_discr_upd_mode' is called to compute the mode. If this is not
     successful `INT_MAX' is returned and `unur_errno' is set to
     `UNUR_ERR_DISTR_GET'.  (There is no difference between the case where no
     routine for computing the mode is available and the case where no mode
     exists for the distribution at all.)

 -- Function: int unur_distr_discr_set_pmfsum (UNUR_DISTR* DISTRIBUTION, double
          SUM)
     Set the sum over the PMF. If `sum' is non-positive, no sum is set and
     `unur_errno' is set to `UNUR_ERR_DISTR_SET'.

     For a distribution object created by the UNU.RAN library of standard
     distributions you always should use the `unur_distr_discr_upd_pmfsum'.
     Otherwise there might be ambiguous side-effects.

 -- Function: int unur_distr_discr_upd_pmfsum (UNUR_DISTR* DISTRIBUTION)
     Recompute the sum over the PMF of the DISTRIBUTION.  In most cases the
     normalization constant is recomputed and thus the sum is 1. This call
     works for distribution objects from the UNU.RAN library of standard
     distributions when the corresponding function is available. When a PV, a
     PMF with finite domain, or a CDF is given, a simple generic function which
     uses a naive summation loop is used. If this computation is not possible,
     an error code is returned and `unur_errno' is set to `UNUR_ERR_DISTR_DATA'.

     The call does not work for distributions from the UNU.RAN library of
     standard distributions with truncated domain when the CDF is not available.

 -- Function: double unur_distr_discr_get_pmfsum (UNUR_DISTR* DISTRIBUTION)
     Get the sum over the PMF of the DISTRIBUTION. If this sum is not known,
     `unur_distr_discr_upd_pmfsum' is called to compute it. If this is not
     successful `UNUR_INFINITY' is returned and `unur_errno' is set to
     `UNUR_ERR_DISTR_GET'.

5 Methods for generating non-uniform random variates
****************************************************

Sampling from a particular distribution with UNU.RAN requires the following
steps:

  1. Create a distribution object (*note Handling distribution objects:
     Distribution_objects.).

  2. Select a method and create a parameter object.

  3. Initizialize the generator object using `unur_init'.

     _Important_: Initialization of the generator object might fail.
     `unur_init' returns a `NULL' pointer then, which *must* not be used for
     sampling.

  4. Draw a sample from the generator object using the corresponding sampling
     function (depending on the type of distribution: univariate continuous,
     univariate discrete, multivariate continuous, and random matrix).

  5. It is possible for a generator object to change the parameters and the
     domain of the underlying distribution. This must be done by extracting
     this object by means of a `unur_get_distr' call and changing the
     distribution using the correspondig set calls, see *note Handling
     distribution objects: Distribution_objects.  The generator object *must*
     then be reinitialized by means of the `unur_reinit' call.

     _Important_: Currently not all methods allow reinitialization, see the
     description of the particular method (keyword Reinit).

     _Important_: Reinitialization of the generator object might fail.  Thus
     one *must* check the return code of the `unur_reinit' call.

     _Important_: When reinitialization fails then sampling routines always
     return INFINITY (for continuous distributions) or `0' (for discrete
     distributions), respectively.  However, it is still possible to change the
     underlying distribution and try to reinitialize again.


5.1 Routines for all generator objects
======================================

Routines for all generator objects.

Function reference
------------------

 -- Function: UNUR_GEN* unur_init (UNUR_PAR* PARAMETERS)
     Initialize a generator object. All necessary information must be stored in
     the parameter object.

     *Important:* If an error has occurred a `NULL' pointer is return. This
     must not be used for the sampling routines (this causes a segmentation
     fault).

     *Always* check whether the call was successful or not!

     _Important:_ This call destroys the PARAMETER object automatically. Thus
     it is not necessary/allowed to free it.

 -- Function: int unur_reinit (UNUR_GEN* GENERATOR)
     Update an existing generator object after the underlying distribution has
     been modified (using `unur_get_distr' together with corresponding set
     calls.  It *must* be executed before sampling using this generator object
     is continued as otherwise it produces an invalid sample or might even
     cause a segmentation fault.

     _Important_: Currently not all methods allow reinitialization, see the
     description of the particular method (keyword Reinit).

     _Important_: Reinitialization of the generator object might fail.  Thus
     one *must* check the return code:

    `UNUR_SUCCESS (0x0u)'
          success (no error)

    `UNUR_ERR_NO_REINIT'
          reinit routine not implemented.

    other values
          some error has occured while trying to reinitialize the generator
          object.

     _Important_: When reinitialization fails then sampling routines always
     return INFINITY (for continuous distributions) or `0' (for discrete
     distributions), respectively.  However, it is still possible to change the
     underlying distribution and try to reinitialize again.

     _Important_: When one tries to run `unur_reinit', but reinitialization is
     not implemented, then the generator object cannot be used any more and
     must be destroyed and a new one has to be built from scratch.

 -- Function: int    unur_sample_discr (UNUR_GEN* GENERATOR)
 -- Function: double unur_sample_cont (UNUR_GEN* GENERATOR)
 -- Function: int    unur_sample_vec (UNUR_GEN* GENERATOR, double* VECTOR)
 -- Function: int    unur_sample_matr (UNUR_GEN* GENERATOR, double* MATRIX)
     Sample from generator object. The three routines depend on the type of the
     generator object (discrete or continuous univariate distribution,
     multivariate distribution, or random matrix).

     _Notice:_ UNU.RAN uses arrays of `double's to handle matrices. There the
     rows of the matrix are stored consecutively.

     _Notice:_ The routines `unur_sample_vec' and `unur_sample_matr' return
     `UNUR_SUCCESS' if generation was successful and some error code otherwise.

     *Important:* These routines do *not* check whether GENERATOR is an invalid
     `NULL' pointer.

 -- Function: double unur_quantile (UNUR_GEN* GENERATOR, double U)
     Compute the U quantile of a continuous distribution using a GENERATOR
     object that implements an (approximate) inversion methods.

     The following methods are currently available:
        * HINV, *note HINV::.

        * NINV, *note NINV::.

        * PINV, *note PINV::.

        * CSTD, *note CSTD::.
          This requires that GENERATOR implements an inversion method.

        * DGT, *note DGT::.
          The return value is (of course) type casted to `double'.

     *Important:* This routine does *not* check whether GENERATOR is an invalid
     `NULL' pointer.

     In case of an error UNUR_INFINITY or INT_MAX (depending on the type of
     GENERATOR) is returned.

 -- Function: void  unur_free (UNUR_GEN* GENERATOR)
     Destroy (free) the given generator object.

 -- Function: const char* unur_gen_info (UNUR_GEN* GENERATOR, int HELP)
     Get a string with informations about the given GENERATOR.  These
     informations allow some fine tuning of the generation method.  If HELP is
     `TRUE', some hints on setting parameters are given.

     This function is intented for using in interactive environments (like R).

     If an error occurs, then `NULL' is returned.

 -- Function: int unur_get_dimension (const UNUR_GEN* GENERATOR)
     Get the number of dimension of a (multivariate) distribution.  For a
     univariate distribution `1' is return.

 -- Function: const char* unur_get_genid (const UNUR_GEN* GENERATOR)
     Get identifier string for generator.

 -- Function: UNUR_DISTR* unur_get_distr (const UNUR_GEN* GENERATOR)
     Get pointer to distribution object from generator object.  This function
     can be used to change the parameters of the distribution and reinitialize
     the generator object.  Notice that currently *not all* generating methods
     have a reinitialize routine.  This function should be used with extreme
     care. Changing the distribution is changed and using the generator object
     without reinitializing might cause wrong samples or segmentation faults.
     Moreover, if the corresponding generator object is freed, the pointer must
     not be used.

     *Important:* The returned distribution object must not be freed. If the
     distribution object is changed then one *must* run `unur_reinit' !

 -- Function: int unur_set_use_distr_privatecopy (UNUR_PAR* PARAMETERS, int
          USE_PRIVATECOPY)
     Set flag whether the generator object should make a private copy of the
     given distribution object or just stores the pointer to this distribution
     object. Values for USE_PRIVATECOPY:
    ``TRUE''
          make a private copy (default)

    ``FALSE''
          do not make a private copy and store pointer to given (external)
          distribution object.

     By default, generator objects keep their own private copy of the given
     distribution object. Thus the generator object can be handled
     independently from other UNU.RAN objects (with uniform random number
     generators as the only exception). When the generator object is
     initialized the given distribution object is cloned and stored.

     However, in some rare situations it can be useful when only the pointer to
     the given distribution object is stored without making a private copy. A
     possible example is when only one random variate has to be drawn from the
     distribution.  This behavior can be achieved when USE_LOCALCOPY is set to
     `FALSE'.

     *Warning!* Using a pointer to the external distribution object instead of a
     private copy must be done with *extreme care*!  When the distrubtion
     object is changed or freed then the generator object does not work any
     more, might case a segmentation fault, or (even worse) produces garbage.
     On the other hand, when the generator object is initialized or used to
     draw a random sampling the distribution object may be changed.

     _Notice:_ The prototypes of all `unur_<method>_new' calls use a `const'
     qualifier for the distribution argument.  However, if USE_PRIVATECOPY is
     set to `FALSE' this qualifier is discarded and the distribution might be
     changed.

     *Important!* If USE_LOCALCOPY is set to `FALSE' and the corresponding
     distribution object is changed then one must run `unur_reinit' on the
     generator object.  (Notice that currently not all generation methods
     support reinitialization.)

     Default: USE_PRIVATECOPY is `TRUE'.

5.2 AUTO  -  Select method automatically
========================================

AUTO selects a an appropriate method for the given distribution object
automatically. There are no parameters for this method, yet. But it is planned
to give some parameter to describe the task for which the random variate
generator is used for and thus make the choice of the generating method more
appropriate.  Notice that the required sampling routine for the generator
object depends on the type of the given distribution object.

   The chosen method also depends on the sample size for which the generator
object will be used. If only a few random variates the order of magnitude of
the sample size should be set via a `unur_auto_set_logss' call.

   IMPORTANT: This is an experimental version and the method chosen may change
in future releases of UNU.RAN.

   For an example see *note As short as possible: Example_0.

How To Use
..........

Create a generator object for the given distribution object.

Function reference
------------------

 -- Function: UNUR_PAR* unur_auto_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_auto_set_logss (UNUR_PAR* PARAMETERS, int LOGSS)
     Set the order of magnitude for the size of the sample that will be
     generated by the generator, i.e., the the common logarithm of the sample
     size.

     Default is 10.

     Notice: This feature will be used in future releases of UNU.RAN only.

5.3 Methods for continuous univariate distributions
===================================================

Overview of methods
-------------------

Methods for continuous univariate distributions
sample with `unur_sample_cont'

method   PDF   dPDF   CDF   mode   area   other
AROU     x     x            [x]           T-concave
HINV     [x]   [x]    x                   
HRB                                       bounded hazard rate
HRD                                       decreasing hazard rate
HRI                                       increasing hazard rate
ITDR     x     x            x             monotone with pole
NINV     [x]          x                   
NROU     x                  [x]           
SROU     x                  x      x      T-concave
SSR      x                  x      x      T-concave
TABL     x                  x      [~]    all local extrema
TDR      x     x                          T-concave
TDRGW    x     x                          T-concave
UTDR     x                  x      ~      T-concave
CSTD                                      build-in standard distribution
CEXT                                      wrapper for external generator

Example
-------

     /* ------------------------------------------------------------- */
     /* File: example_cont.c                                          */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */

     /* Example how to sample from a continuous univariate            */
     /* distribution.                                                 */
     /*                                                               */
     /* We build a distribution object from scratch and sample.       */

     /* ------------------------------------------------------------- */

     /* Define the PDF and dPDF of our distribution.                  */
     /*                                                               */
     /* Our distribution has the PDF                                  */
     /*                                                               */
     /*          /  1 - x*x  if |x| <= 1                              */
     /*  f(x) = <                                                     */
     /*          \  0        otherwise                                */
     /*                                                               */

     /* The PDF of our distribution:                                  */
     double mypdf( double x, const UNUR_DISTR *distr )
          /* The second argument (`distr') can be used for parameters */
          /* for the PDF. (We do not use parameters in our example.)  */
     {
       if (fabs(x) >= 1.)
         return 0.;
       else
         return (1.-x*x);
     } /* end of mypdf() */

     /* The derivative of the PDF of our distribution:                */
     double mydpdf( double x, const UNUR_DISTR *distr )
     {
       if (fabs(x) >= 1.)
         return 0.;
       else
         return (-2.*x);
     } /* end of mydpdf() */

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;     /* loop variable                                 */
       double x;     /* will hold the random number                   */

       /* Declare the three UNURAN objects.                           */
       UNUR_DISTR *distr;    /* distribution object                   */
       UNUR_PAR   *par;      /* parameter object                      */
       UNUR_GEN   *gen;      /* generator object                      */

       /* Create a new distribution object from scratch.              */

       /* Get empty distribution object for a continuous distribution */
       distr = unur_distr_cont_new();

       /* Fill the distribution object -- the provided information    */
       /* must fulfill the requirements of the method choosen below.  */
       unur_distr_cont_set_pdf(distr,  mypdf);     /* PDF             */
       unur_distr_cont_set_dpdf(distr, mydpdf);    /* its derivative  */
       unur_distr_cont_set_mode(distr, 0.);        /* mode            */
       unur_distr_cont_set_domain(distr, -1., 1.); /* domain          */

       /* Choose a method: TDR.                                       */
       par = unur_tdr_new(distr);

       /* Set some parameters of the method TDR.                      */
       unur_tdr_set_variant_gw(par);
       unur_tdr_set_max_sqhratio(par, 0.90);
       unur_tdr_set_c(par, -0.5);
       unur_tdr_set_max_intervals(par, 100);
       unur_tdr_set_cpoints(par, 10, NULL);

       /* Create the generator object.                                */
       gen = unur_init(par);

       /* Notice that this call has also destroyed the parameter      */
       /* object `par' as a side effect.                              */

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* It is possible to reuse the distribution object to create   */
       /* another generator object. If you do not need it any more,   */
       /* it should be destroyed to free memory.                      */
       unur_distr_free(distr);

       /* Now you can use the generator object `gen' to sample from   */
       /* the distribution. Eg.:                                      */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* When you do not need the generator object any more, you     */
       /* can destroy it.                                             */
       unur_free(gen);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

Example (String API)
--------------------

     /* ------------------------------------------------------------- */
     /* File: example_cont_str.c                                      */
     /* ------------------------------------------------------------- */
     /* String API.                                                   */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */

     /* Example how to sample from a continuous univariate            */
     /* distribution.                                                 */

     /* We use a generic distribution object and sample.              */
     /*                                                               */
     /* The PDF of our distribution is given by                       */
     /*                                                               */
     /*          /  1 - x*x  if |x| <= 1                              */
     /*  f(x) = <                                                     */
     /*          \  0        otherwise                                */
     /*                                                               */

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;     /* loop variable                                 */
       double x;     /* will hold the random number                   */

       /* Declare UNURAN generator object.                            */
       UNUR_GEN   *gen;      /* generator object                      */

       /* Create the generator object.                                */
       /* Use a generic continuous distribution.                      */
       /* Choose a method: TDR.                                       */
       gen = unur_str2gen(
                "distr = cont; pdf=\"1-x*x\"; domain=(-1,1); mode=0. & \
                 method=tdr; variant_gw; max_sqhratio=0.90; c=-0.5; \
                 max_intervals=100; cpoints=10");

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* Now you can use the generator object `gen' to sample from   */
       /* the distribution. Eg.:                                      */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* When you do not need the generator object any more, you     */
       /* can destroy it.                                             */
       unur_free(gen);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

5.3.1 AROU  -  Automatic Ratio-Of-Uniforms method
-------------------------------------------------

Required:
     T-concave PDF, dPDF

Optional:
     mode

Speed:
     Set-up: slow, Sampling: fast

Reinit:
     not implemented

Reference:
     [LJa00]



   AROU is a variant of the ratio-of-uniforms method that uses the fact that
the transformed region is convex for many distributions.  It works for all
T-concave distributions with T(x) = -1/sqrt(x).

   It is possible to use this method for correlation induction by setting an
auxiliary uniform random number generator via the `unur_set_urng_aux' call.
(Notice that this must be done after a possible `unur_set_urng' call.)  When an
auxiliary generator is used then the number of used uniform random numbers that
is used up for one generated random variate is constant and equal to 1.

   There exists a test mode that verifies whether the conditions for the method
are satisfied or not while sampling. It can be switched on by calling
`unur_arou_set_verify' and `unur_arou_chg_verify', respectively.  Notice
however that sampling is (much) slower then.

   For densities with modes not close to 0 it is suggested to set either the
mode or the center of the distribution by the `unur_distr_cont_set_mode' or
`unur_distr_cont_set_center' call.  The latter is the approximate location of
the mode or the mean of the distribution. This location provides some
information about the main part of the PDF and is used to avoid numerical
problems.

Function reference
------------------

 -- Function: UNUR_PAR* unur_arou_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_arou_set_usedars (UNUR_PAR* PARAMETERS, int USEDARS)
     If USEDARS is set to `TRUE', "derandomized adaptive rejection sampling"
     (DARS) is used in setup.  Segments where the area between hat and squeeze
     is too large compared to the average area between hat and squeeze over all
     intervals are split.  This procedure is repeated until the ratio between
     area below squeeze and area below hat exceeds the bound given by
     `unur_arou_set_max_sqhratio' call or the maximum number of segments is
     reached. Moreover, it also aborts when no more segments can be found for
     splitting.

     Segments are split such that the angle of the segments are halved
     (corresponds to arc-mean rule of method TDR (*note TDR::)).

     Default is `TRUE'.

 -- Function: int unur_arou_set_darsfactor (UNUR_PAR* PARAMETERS, double FACTOR)
     Set factor for "derandomized adaptive rejection sampling".  This factor is
     used to determine the segments that are "too large", that is, all segments
     where the area between squeeze and hat is larger than FACTOR times the
     average area over all intervals between squeeze and hat.  Notice that all
     segments are split when FACTOR is set to `0.', and that there is no
     splitting at all when FACTOR is set to `UNUR_INFINITY'.

     Default is `0.99'. There is no need to change this parameter.

 -- Function: int unur_arou_set_max_sqhratio (UNUR_PAR* PARAMETERS, double
          MAX_RATIO)
     Set upper bound for the ratio (area inside squeeze) / (area inside
     envelope).  It must be a number between 0 and 1.  When the ratio exceeds
     the given number no further construction points are inserted via adaptive
     rejection sampling.  Use `0' if no construction points should be added
     after the setup.  Use `1' if adding new construction points should not be
     stopped until the maximum number of construction points is reached.

     Default is `0.99'.

 -- Function: double unur_arou_get_sqhratio (const UNUR_GEN* GENERATOR)
     Get the current ratio (area inside squeeze) / (area inside envelope) for
     the generator.  (In case of an error `UNUR_INFINITY' is returned.)

 -- Function: double unur_arou_get_hatarea (const UNUR_GEN* GENERATOR)
     Get the area below the hat for the generator.  (In case of an error
     `UNUR_INFINITY' is returned.)

 -- Function: double unur_arou_get_squeezearea (const UNUR_GEN* GENERATOR)
     Get the area below the squeeze for the generator.  (In case of an error
     `UNUR_INFINITY' is returned.)

 -- Function: int unur_arou_set_max_segments (UNUR_PAR* PARAMETERS, int
          MAX_SEGS)
     Set maximum number of segements.  No construction points are added _after_
     the setup when the number of segments succeeds MAX_SEGS.

     Default is `100'.

 -- Function: int unur_arou_set_cpoints (UNUR_PAR* PARAMETERS, int N_STP, const
          DOUBLE* STP)
     Set construction points for enveloping polygon.  If STP is `NULL', then a
     heuristical rule of thumb is used to get N_STP construction points.  This
     is the default behavior when this routine is not called.  The (default)
     number of construction points is `30', then.

 -- Function: int unur_arou_set_usecenter (UNUR_PAR* PARAMETERS, int USECENTER)
     Use the center as construction point.  Default is `TRUE'.

 -- Function: int unur_arou_set_guidefactor (UNUR_PAR* PARAMETERS, double
          FACTOR)
     Set factor for relative size of the guide table for indexed search (see
     also method DGT *note DGT::). It must be greater than or equal to `0'.
     When set to `0', then sequential search is used.

     Default is `2'.

 -- Function: int unur_arou_set_verify (UNUR_PAR* PARAMETERS, int VERIFY)
 -- Function: int unur_arou_chg_verify (UNUR_GEN* GENERATOR, int VERIFY)
     Turn verifying of algorithm while sampling on/off.  If the condition
     squeeze(x) <= PDF(x) <= hat(x) is violated for some x then `unur_errno' is
     set to `UNUR_ERR_GEN_CONDITION'. However notice that this might happen due
     to round-off errors for a few values of x (less than 1%).

     Default is `FALSE'.

 -- Function: int unur_arou_set_pedantic (UNUR_PAR* PARAMETERS, int PEDANTIC)
     Sometimes it might happen that `unur_init' has been executed successfully.
     But when additional construction points are added by adaptive rejection
     sampling, the algorithm detects that the PDF is not T-concave.

     With PEDANTIC being `TRUE', the sampling routine is then exchanged by a
     routine that simply returns `UNUR_INFINITY'. Otherwise the new point is
     not added to the list of construction points. At least the hat function
     remains T-concave.

     Setting PEDANTIC to `FALSE' allows sampling from a distribution which is
     "almost" T-concave and small errors are tolerated. However it might happen
     that the hat function cannot be improved significantly. When the hat
     function that has been constructed by the `unur_init' call is extremely
     large then it might happen that the generation times are extremely high
     (even hours are possible in extremely rare cases).

     Default is `FALSE'.

5.3.2 ARS  -  Adaptive Rejection Sampling
-----------------------------------------

Required:
     concave logPDF, derivative of logPDF

Optional:
     mode

Speed:
     Set-up: fast, Sampling: slow

Reinit:
     supported

Reference:
     [GWa92] [HLD04: Cha.4]



   ARS is an acceptance/rejection method that uses the concavity of the
log-density function to construct hat function and squeezes automatically.  It
is very similar to method TDR (*note TDR::) with variant GW, parameter `c = 0',
and DARS switched off.  Moreover, method ARS requires the logPDF and its
derivative dlogPDF to run. On the other hand, it is designed to draw only a
(very) small samples and it is much more robust against densities with very
large or small areas below the PDF as it occurs, for example, in conditional
distributions of (high dimensional) multivariate distributions.  Additionally,
it can be re-initialized when the underlying distribution has been modified.
Thus it is well suited for Gibbs sampling.

   Notice, that method ARS is a restricted version of TDR. If the full
functionally of Transformed Density Rejection is needed use method *note TDR::.

How To Use
..........

Method ARS is designed for distributions with log-concave densities. To use
this method you need a distribution object with the logarithm of the PDF and
its derivative given.

   The number of construction points as well as a set of such points can be
provided using `unur_ars_set_cpoints'.  Notice that addition construction
points are added by means of adaptive rejection sampling until the maximal
number of intervals given by `unur_ars_set_max_intervals' is reached.

   A generated distribution object can be reinitialized using the `unur_reinit'
call. When `unur_reinit' is called construction points for the new generator
are necessary. There are two options: Either the same construction points as
for the initial generator (given by a `unur_ars_set_cpoints' call) are used
(this is the default), or percentiles of the old hat function can be used.
This can be set or changed using `unur_ars_set_reinit_percentiles' and
`unur_ars_chg_reinit_percentiles'.  This feature is usefull when the underlying
distribution object is only moderately changed. (An example is Gibbs sampling
with small correlations.)

   There exists a test mode that verifies whether the conditions for the method
are satisfied or not. It can be switched on by calling `unur_ars_set_verify' and
`unur_ars_chg_verify', respectively.  Notice however that sampling is (much)
slower then.

   Method ARS aborts after a given number of iterations and return
UNUR_INFINITY to prevent (almost) infinite loops. This might happen when the
starting hat is much too large and it is not possible to insert new
construction points due to severe numerical errors or (more likely) the given
PDF is not log-concave. This maximum number of iterations can be set by means
of a `unur_ars_set_max_iter' call.

Function reference
------------------

 -- Function: UNUR_PAR* unur_ars_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_ars_set_max_intervals (UNUR_PAR* PARAMETERS, int MAX_IVS)
     Set maximum number of intervals.  No construction points are added after
     the setup when the number of intervals suceeds MAX_IVS.  It is increased
     automatically to twice the number of construction points if this is larger.

     Default is `200'.

 -- Function: int unur_ars_set_cpoints (UNUR_PAR* PARAMETERS, int N_CPOINTS,
          const DOUBLE* CPOINTS)
     Set construction points for the hat function. If CPOINTS is `NULL' then a
     heuristic rule of thumb is used to get N_CPOINTS construction points. This
     is the default behavior.  N_CPOINTS should be at least `2', otherwise
     defaults are used.

     The default number of construction points is 2.

 -- Function: int unur_ars_set_reinit_percentiles (UNUR_PAR* PARAMETERS, int
          N_PERCENTILES, const DOUBLE* PERCENTILES)
 -- Function: int unur_ars_chg_reinit_percentiles (UNUR_GEN* GENERATOR, int
          N_PERCENTILES, const DOUBLE* PERCENTILES)
     By default, when the GENERATOR object is reinitialized, it used the same
     construction points as for the initialization procedure.  Often the
     underlying distribution object has been changed only moderately. For
     example, the full conditional distribution of a multivariate distribution.
     In this case it might be more appropriate to use percentilesm of the hat
     function for the last (unchanged) distribution. PERCENTILES must then be a
     pointer to an ordered array of numbers between `0.01' and `0.99'.  If
     PERCENTILES is `NULL', then a heuristic rule of thumb is used to get
     N_PERCENTILES values for these percentiles.  Notice that N_PERCENTILES
     must be at least `2', otherwise defaults are used.  (Then the first and
     third quartiles are used by default.)

 -- Function: int unur_ars_set_reinit_ncpoints (UNUR_PAR* PARAMETERS, int
          NCPOINTS)
 -- Function: int unur_ars_chg_reinit_ncpoints (UNUR_GEN* GENERATOR, int
          NCPOINTS)
     When reinit fails with the given construction points or the percentiles of
     the old hat function, another trial is undertaken with NCPOINTS
     construction points. NCPOINTS must be at least `10'.

     Default: `30'

 -- Function: int unur_ars_set_max_iter (UNUR_PAR* PARAMETERS, int MAX_ITER)
     The rejection loop stops after MAX_ITER iterations and return
     UNUR_INFINITY.

     Default: `10000'

 -- Function: int unur_ars_set_verify (UNUR_PAR* PARAMETERS, int VERIFY)
 -- Function: int unur_ars_chg_verify (UNUR_GEN* GENERATOR, int VERIFY)
     Turn verifying of algorithm while sampling on/off.  If the condition
     squeeze(x) <= PDF(x) <= hat(x) is violated for some x then `unur_errno' is
     set to `UNUR_ERR_GEN_CONDITION'. However notice that this might happen due
     to round-off errors for a few values of x (less than 1%).

     Default is `FALSE'.

 -- Function: int unur_ars_set_pedantic (UNUR_PAR* PARAMETERS, int PEDANTIC)
     Sometimes it might happen that `unur_init' has been executed successfully.
     But when additional construction points are added by adaptive rejection
     sampling, the algorithm detects that the PDF is not log-concave.

     With PEDANTIC being `TRUE', the sampling routine is exchanged by a routine
     that simply returns `UNUR_INFINITY'. Otherwise the new point is not added
     to the list of construction points. At least the hat function remains
     log-concave.

     Setting PEDANTIC to `FALSE' allows sampling from a distribution which is
     "almost" log-concave and small errors are tolerated. However it might
     happen that the hat function cannot be improved significantly. When the
     hat functions that has been constructed by the `unur_init' call is
     extremely large then it might happen that the generation times are
     extremely high (even hours are possible in extremely rare cases).

     Default is `FALSE'.

 -- Function: double unur_ars_get_loghatarea (const UNUR_GEN* GENERATOR)
     Get the logarithm of area below the hat for the generator.  (In case of an
     error `UNUR_INFINITY' is returned.)

 -- Function: double unur_ars_eval_invcdfhat (const UNUR_GEN* GENERATOR, double
          U)
     Evaluate the inverse of the CDF of the hat distribution at U.

     If U is out of the domain [0,1] then `unur_errno' is set to
     `UNUR_ERR_DOMAIN' and the respective bound of the domain of the
     distribution are returned (which is `-UNUR_INFINITY' or `UNUR_INFINITY' in
     the case of unbounded domains).

5.3.3 CEXT  -  wrapper for Continuous EXTernal generators
---------------------------------------------------------

Required:
     routine for sampling continuous random variates

Speed:
     depends on external generator

Reinit:
     supported


   Method CEXT is a wrapper for external generators for continuous univariate
distributions. It allows the usage of external random variate generators within
the UNU.RAN framework.

How To Use
..........

The following steps are required to use some external generator within the
UNU.RAN framework (some of these are optional):

  1. Make an empty generator object using a `unur_cext_new' call.  The argument
     DISTRIBUTION is optional and can be replaced by `NULL'. However, it is
     required if you want to pass parameters of the generated distribution to
     the external generator or for running some validation tests provided by
     UNU.RAN.

  2. Create an initialization routine of type `int (*init)(UNUR_GEN *gen)' and
     plug it into the generator object using the `unur_cext_set_init' call.
     Notice that the INIT routine must return `UNUR_SUCCESS' when it has been
     executed successfully and `UNUR_FAILURE' otherwise.  It is possible to get
     the size of and the pointer to the array of parameters of the underlying
     distribution object by the respective calls `unur_cext_get_ndistrparams'
     and `unur_cext_get_distrparams'.  Parameters for the external generator
     that are computed in the INIT routine can be stored in a single array or
     structure which is available by the `unur_cext_get_params' call.

     Using an INIT routine is optional and can be omitted.

  3. Create a sampling routine of type `double (*sample)(UNUR_GEN *gen)' and
     plug it into the generator object using the `unur_cext_set_sample' call.

     Uniform random numbers are provided by the `unur_sample_urng' call. Do not
     use your own implementation of a uniform random number generator directly.
     If you want to use your own random number generator we recommend to use
     the UNU.RAN interface (see *note Using uniform random number generators:
     URNG.).

     The array or structure that contains parameters for the external generator
     that are computed in the INIT routine are available using the
     `unur_cext_get_params' call.

     Using a SAMPLE routine is of course obligatory.

   It is possible to change the parameters and the domain of the chosen
distribution and run `unur_reinit' to reinitialize the generator object. The
INIT routine is then called again.

   Here is a short example that demonstrates the application of this method by
means of the exponential distribution:

     /* ------------------------------------------------------------- */
     /* File: example_cext.c                                          */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */

     /* This example shows how an external generator for the          */
     /* exponential distribution with one scale parameter can be      */
     /* used within the UNURAN framework.                             */
     /*                                                               */
     /* Notice, that this example does not provide the simplest       */
     /* solution.                                                     */

     /* ------------------------------------------------------------- */
     /* Initialization routine.                                       */
     /*                                                               */
     /*   Here we simply read the scale parameter of the exponential  */
     /*   distribution and store it in an array for parameters of     */
     /*   the external generator.                                     */
     /*   [ Of course we could do this in the sampling routine as     */
     /*   and avoid the necessity of this initialization routine. ]   */

     int exponential_init (UNUR_GEN *gen)
     {
       /* Get pointer to parameters of exponential distribution       */
       double *params = unur_cext_get_distrparams(gen);

       /* The scale parameter is the first entry (see manual)         */
       double lambda = (params) ? params[0] : 1.;

       /* Get array to store this parameter for external generator    */
       double *genpar = unur_cext_get_params(gen, sizeof(double));
       genpar[0] = lambda;

       /* Executed successfully                                       */
       return UNUR_SUCCESS;
     }

     /* ------------------------------------------------------------- */
     /* Sampling routine.                                             */
     /*                                                               */
     /*   Contains the code for the external generator.               */

     double exponential_sample (UNUR_GEN *gen)
     {
       /* Get scale parameter                                         */
       double *genpar = unur_cext_get_params(gen,0);
       double lambda = genpar[0];

       /* Sample a uniformly distributed random number                */
       double U = unur_sample_urng(gen);

       /* Transform into exponentially distributed random variate     */
       return ( -log(1. - U) * lambda );
     }

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;     /* loop variable                                 */
       double x;     /* will hold the random number                   */

       /* Declare the three UNURAN objects.                           */
       UNUR_DISTR *distr;    /* distribution object                   */
       UNUR_PAR   *par;      /* parameter object                      */
       UNUR_GEN   *gen;      /* generator object                      */

       /* Use predefined exponential distribution with scale param. 2 */
       double fpar[1] = { 2. };
       distr = unur_distr_exponential(fpar, 1);

       /* Use method CEXT                                             */
       par = unur_cext_new(distr);

       /* Set initialization and sampling routines.                   */
       unur_cext_set_init(par, exponential_init);
       unur_cext_set_sample(par, exponential_sample);

       /* Create the generator object.                                */
       gen = unur_init(par);

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* It is possible to reuse the distribution object to create   */
       /* another generator object. If you do not need it any more,   */
       /* it should be destroyed to free memory.                      */
       unur_distr_free(distr);

       /* Now you can use the generator object `gen' to sample from   */
       /* the standard Gaussian distribution.                         */
       /* Eg.:                                                        */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* When you do not need the generator object any more, you     */
       /* can destroy it.                                             */
       unur_free(gen);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

Function reference
------------------

 -- Function: UNUR_PAR* unur_cext_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for new generator.

 -- Function: int unur_cext_set_init (UNUR_PAR* PARAMETERS, int (*
          INIT)(UNUR_GEN* gen ))
     Set initialization routine for external generator. Inside the

     _Important:_ The routine INIT must return `UNUR_SUCCESS' when the
     generator was initialized successfully and `UNUR_FAILURE' otherwise.

     Parameters that are computed in the INIT routine can be stored in an array
     or structure that is avaiable by means of the `unur_cext_get_params' call.
     Parameters of the underlying distribution object can be obtained by the
     `unur_cext_get_distrparams' call.

 -- Function: int unur_cext_set_sample (UNUR_PAR* PARAMETERS, double (*
          SAMPLE)(UNUR_GEN* gen ))
     Set sampling routine for external generator.

     _Important:_ Use `unur_sample_urng(gen)' to get a uniform random number.
     The pointer to the array or structure that contains the parameters that
     are precomputed in the INIT routine are available by
     `unur_cext_get_params(gen,0)'.  Additionally one can use the
     `unur_cext_get_distrparams' call.

 -- Function: void* unur_cext_get_params (UNUR_GEN* GENERATOR, size_t SIZE)
     Get pointer to memory block for storing parameters of external generator.
     A memory block of size SIZE is automatically (re-) allocated if necessary
     and the pointer to this block is stored in the GENERATOR object. If one
     only needs the pointer to this memory block set SIZE to `0'.

     Notice, that SIZE is the size of the memory block and not the length of an
     array.

     _Important:_ This rountine should only be used in the initialization and
     sampling routine of the external generator.

 -- Function: double* unur_cext_get_distrparams (UNUR_GEN* GENERATOR)
 -- Function: int unur_cext_get_ndistrparams (UNUR_GEN* GENERATOR)
     Get size of and pointer to array of parameters of underlying distribution
     in GENERATOR object.

     _Important:_ These rountines should only be used in the initialization and
     sampling routine of the external generator.

5.3.4 CSTD  -  Continuous STandarD distributions
------------------------------------------------

Required:
     standard distribution from UNU.RAN library (*note Standard distributions:
     Stddist.) or continuous distribution with inverse CDF.

Speed:
     Set-up: fast, Sampling: depends on distribution and generator

Reinit:
     supported


   CSTD is a wrapper for special generators for continuous univariate standard
distributions. It only works for distributions in the UNU.RAN library of
standard distributions (*note Standard distributions: Stddist.)  or for
continuous distributions where the inverse CDF is given.  If a distribution
object is provided that is build from scratch, it must provide the inverse CDF.
Then CSTD implements the inversion method. Otherwise, the `NULL' pointer is
returned.

   For some distributions more than one special generator is possible.

How To Use
..........

Create a distribution object for a standard distribution from the UNU.RAN
library (*note Standard distributions: Stddist.), or create a continuous
distribution object and set the function for the inverse CDF using
`unur_distr_cont_set_invcdf'.  For some distributions more than one special
generator (_variants_) is possible. These can be choosen by a
`unur_cstd_set_variant' call. For possible variants *note Standard
distributions: Stddist.  However the following are common to all distributions:

`UNUR_STDGEN_DEFAULT'
     the default generator.

`UNUR_STDGEN_FAST'
     the fastest available special generator.

`UNUR_STDGEN_INVERSION'
     the inversion method (if available).

   Notice that the variant `UNUR_STDGEN_FAST' for a special generator may be
slower than one of the universal algorithms!  Additional variants may exist for
particular distributions.

   Sampling from truncated distributions (which can be constructed by changing
the default domain of a distribution by means of `unur_distr_cont_set_domain' or
`unur_cstd_chg_truncated' calls) is possible but requires the inversion method.
Moreover the CDF of the distribution must be implemented.

   It is possible to change the parameters and the domain of the chosen
distribution and run `unur_reinit' to reinitialize the generator object.

Function reference
------------------

 -- Function: UNUR_PAR* unur_cstd_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for new generator. It requires a distribution object
     for a continuous univariant distribution from the UNU.RAN library of
     standard distributions (*note Standard distributions: Stddist.).

     Using a truncated distribution is allowed only if the inversion method is
     available and selected by the `unur_cstd_set_variant' call immediately
     after creating the parameter object.  Use a `unur_distr_cont_set_domain'
     call to get a truncated distribution.  To change the domain of a
     (truncated) distribution of a generator use the `unur_cstd_chg_truncated'
     call.

 -- Function: int unur_cstd_set_variant (UNUR_PAR* PARAMETERS, unsigned VARIANT)
     Set variant (special generator) for sampling from a given distribution.
     For possible variants *note Standard distributions: Stddist.

     Common variants are `UNUR_STDGEN_DEFAULT' for the default generator,
     `UNUR_STDGEN_FAST' for (one of the) fastest implemented special
     generators, and `UNUR_STDGEN_INVERSION' for the inversion method (if
     available).  If the selected variant number is not implemented, then an
     error code is returned and the variant is not changed.

 -- Function: int unur_cstd_chg_truncated (UNUR_GEN* GENERATOR, double LEFT,
          double RIGHT)
     Change left and right border of the domain of the (truncated) distribution.
     This is only possible if the inversion method is used.  Otherwise this
     call has no effect and an error code is returned.

     Notice that the given truncated domain must be a subset of the domain of
     the given distribution. The generator always uses the intersection of the
     domain of the distribution and the truncated domain given by this call.

     It is not required to run `unur_reinit' after this call has been used.

     _Important:_ If the CDF is (almost) the same for LEFT and RIGHT and
     (almost) equal to `0' or `1', then the truncated domain is not chanced and
     the call returns an error code.

     _Notice:_ If the parameters of the distribution has been changed it is
     recommended to set the truncated domain again, since the former call might
     change the domain of the distribution but not update the values for the
     boundaries of the truncated distribution.

5.3.5 HINV  -  Hermite interpolation based INVersion of CDF
-----------------------------------------------------------

Required:
     CDF

Optional:
     PDF, dPDF

Speed:
     Set-up: (very) slow, Sampling: (very) fast

Reinit:
     supported

Reference:
     [HLa03] [HLD04: Sect.7.2; Alg.7.1]



   HINV is a variant of numerical inversion, where the inverse CDF is
approximated using Hermite interpolation, i.e., the interval [0,1] is split
into several intervals and in each interval the inverse CDF is approximated by
polynomials constructed by means of values of the CDF and PDF at interval
boundaries. This makes it possible to improve the accuracy by splitting a
particular interval without recomputations in unaffected intervals. Three types
of splines are implemented: linear, cubic, and quintic interpolation. For
linear interpolation only the CDF is required. Cubic interpolation also
requires PDF and quintic interpolation PDF and its derivative.

   These splines have to be computed in a setup step. However, it only works
for distributions with bounded domain; for distributions with unbounded domain
the tails are chopped off such that the probability for the tail regions is
small compared to the given u-resolution.

   The method is not exact, as it only produces random variates of the
approximated distribution. Nevertheless, the maximal numerical error in
"u-direction" (i.e. |U-CDF(X)|, for X = "approximate inverse CDF"(U)
|U-CDF(X)|) can be set to the required resolution (within machine precision).
Notice that very small values of the u-resolution are possible but may increase
the cost for the setup step.

   As the possible maximal error is only estimated in the setup it may be
necessary to set some special design points for computing the Hermite
interpolation to guarantee that the maximal u-error can not be bigger than
desired. Such points are points where the density is not differentiable or has
a local extremum. Notice that there is no necessity to do so. However, if you
do not provide these points to the algorithm there might be a small chance that
the approximation error is larger than the given u-resolution, or that the
required number of intervals is larger than necessary.

How To Use
..........

HINV works for continuous univariate distribution objects with given CDF and
(optional) PDF. It uses Hermite interpolation of order 1, 3 [default] or 5. The
order can be set by means of `unur_hinv_set_order'.  For distributions with
unbounded domains the tails are chopped off such that the probability for the
tail regions is small compared to the given u-resulution. For finding these cut
points the algorithm starts with the region `[-1.e20,1.e20]'. For the
exceptional case where this might be too small (or one knows this region and
wants to avoid this search heuristics) it can be directly set via a
`unur_hinv_set_boundary' call.

   It is possible to use this method for generating from truncated
distributions. It even can be changed for an existing generator object by an
`unur_hinv_chg_truncated' call.

   This method is not exact, as it only produces random variates of the
approximated distribution. Nevertheless, the numerical error in "u-direction"
(i.e. |U-CDF(X)|, for X = "approximate inverse CDF"(U) |U-CDF(X)|) can be
controlled by means of `unur_hinv_set_u_resolution'.  The possible maximal
error is only estimated in the setup. Thus it might be necessary to set some
special design points for computing the Hermite interpolation to guarantee that
the maximal u-error can not be bigger than desired. Such points (e.g. extremal
points of the PDF, points with infinite derivative) can be set using using the
`unur_hinv_set_cpoints' call.  If the mode for a unimodal distribution is set
in the distribution object this mode is automatically used as design-point if
the `unur_hinv_set_cpoints' call is not used.

   As already mentioned the maximal error of this approximation is only
estimated. If this error is crucial for an application we recommend to compute
this error using `unur_hinv_estimate_error' which runs a small Monte Carlo
simulation.

   It is possible to change the parameters and the domain of the chosen
distribution and run `unur_reinit' to reinitialize the generator object.  The
values given by the last `unur_hinv_chg_truncated' call will be then changed to
the values of the domain of the underlying distribution object. Moreover,
starting construction points (nodes) that are given by a `unur_hinv_set_cpoints'
call are ignored when `unur_reinit' is called.  It is important to note that
for a distribution from the UNU.RAN library of standard distributions (*note
Standard distributions: Stddist.)  the normalization constant has to be updated
using the `unur_distr_cont_upd_pdfarea' call whenever its parameters have been
changed by means of a `unur_distr_cont_set_pdfparams' call.

Function reference
------------------

 -- Function: UNUR_PAR* unur_hinv_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_hinv_set_order (UNUR_PAR* PARAMETERS, int ORDER)
     Set order of Hermite interpolation. Valid orders are `1', `3', and `5'.
     Notice that ORDER greater than `1' requires the density of the
     distribution, and ORDER greater than `3' even requires the derivative of
     the density. Using ORDER `1' results for most distributions in a huge
     number of intervals and is therefore not recommended. If the maximal error
     in u-direction is very small (say smaller than `1.e-10'), ORDER `5' is
     recommended as it leads to considerably fewer design points, as long there
     are no poles or heavy tails.

     _Remark:_ When the target distribution has poles or (very) heavy tails
     ORDER `5' (i.e., quintic interpolation) is numerically less stable and
     more sensitive to round-off errors than ORDER `3' (i.e., cubic
     interpolation).

     Default is `3' if the density is given and `1' otherwise.

 -- Function: int unur_hinv_set_u_resolution (UNUR_PAR* PARAMETERS, double
          U_RESOLUTION)
     Set maximal error in u-direction. However, the given u-error must not be
     smaller than machine epsilon (`DBL_EPSILON') and should not be too close
     to this value. As the resolution of most uniform random number sources is
     2^(-32) = `2.3e-10', a value of `1.e-10' leads to an inversion algorithm
     that could be called exact. For most simulations slightly bigger values
     for the maximal error are enough as well.

     Remark: The u-error might become larger than U_RESOLUTION due to rescaling
     of floating point numbers when the domain of the distribution is truncated
     by a `unur_hinv_chg_truncated' call.

     Default is `1.e-10'.

 -- Function: int unur_hinv_set_cpoints (UNUR_PAR* PARAMETERS, const DOUBLE*
          STP, int N_STP)
     Set starting construction points (nodes) for Hermite interpolation.

     As the possible maximal error is only estimated in the setup it may be
     necessary to set some special design points for computing the Hermite
     interpolation to guarantee that the maximal u-error can not be bigger than
     desired. We suggest to include as special design points all local extrema
     of the density, all points where the density is not differentiable, and
     isolated points inside of the domain with density 0.  If there is an
     interval with density constant equal to 0 inside of the given domain of
     the density, both endpoints of this interval should be included as special
     design points. Notice that there is no necessity to do so. However, if
     these points are not provided to the algorithm the approximation error
     might be larger than the given u-resolution, or the required number of
     intervals could be larger than necessary.

     _Important_: Notice that the given points must be in increasing order and
     they must be disjoint.

     _Important_: The boundary point of the computational region must not be
     given in this list!  Points outside the boundary of the computational
     region are ignored.

     Default is for unimodal densities - if known - the mode of the density, if
     it is not equal to the border of the domain.

 -- Function: int unur_hinv_set_boundary (UNUR_PAR* PARAMETERS, double LEFT,
          double RIGHT)
     Set the left and right boundary of the computational interval.  Of course
     `+/- UNUR_INFINITY' is not allowed.  If the CDF at LEFT and RIGHT is not
     close to the respective values `0.' and `1.' then this interval is
     increased by a (rather slow) search algorithm.

     _Important_: This call does not change the domain of the given
     distribution itself. But it restricts the domain for the resulting random
     variates.

     Default is `1.e20'.

 -- Function: int unur_hinv_set_guidefactor (UNUR_PAR* PARAMETERS, double
          FACTOR)
     Set factor for relative size of the guide table for indexed search (see
     also method DGT *note DGT::). It must be greater than or equal to `0'.
     When set to `0', then sequential search is used.

     Default is `1'.

 -- Function: int unur_hinv_set_max_intervals (UNUR_PAR* PARAMETERS, int
          MAX_IVS)
     Set maximum number of intervals. No generator object is created if the
     necessary number of intervals for the Hermite interpolation exceeds
     MAX_IVS. It is used to prevent the algorithm to eat up all memory for very
     badly shaped CDFs.

     Default is `1000000' (1.e6).

 -- Function: int unur_hinv_get_n_intervals (const UNUR_GEN* GENERATOR)
     Get number of nodes (design points) used for Hermite interpolation in the
     generator object. The number of intervals is the number of nodes minus 1.
     It returns an error code in case of an error.

 -- Function: double unur_hinv_eval_approxinvcdf (const UNUR_GEN* GENERATOR,
          double U)
     Evaluate Hermite interpolation of inverse CDF at U.  If U is out of the
     domain [0,1] then `unur_errno' is set to `UNUR_ERR_DOMAIN' and the
     respective bound of the domain of the distribution are returned (which is
     `-UNUR_INFINITY' or `UNUR_INFINITY' in the case of unbounded domains).

     _Notice_: When the domain has been truncated by a `unur_hinv_chg_truncated'
     call then the inverse CDF of the truncated distribution is returned.

 -- Function: int unur_hinv_chg_truncated (UNUR_GEN* GENERATOR, double LEFT,
          double RIGHT)
     Changes the borders of the domain of the (truncated) distribution.

     Notice that the given truncated domain must be a subset of the domain of
     the given distribution. The generator always uses the intersection of the
     domain of the distribution and the truncated domain given by this call.
     The tables of splines are not recomputed.  Thus it might happen that the
     relative error for the generated variates from the truncated distribution
     is greater than the bound for the non-truncated distribution. This call
     also fails when the CDF values of the boundary points are too close, i.e.
     when only a few different floating point numbers would be computed due to
     round-off errors with floating point arithmetic.

     Remark: The u-error might become larger than the U_RESOLUTION given by a
     `unur_hinv_set_u_resolution' call due to rescaling of floating point
     numbers when the domain of the distribution is truncated.

     When failed an error code is returned.

     _Important_: Always check the return code since the domain is not changed
     in case of an error.

 -- Function: int unur_hinv_estimate_error (const UNUR_GEN* GENERATOR, int
          SAMPLESIZE, double* MAX_ERROR, double* MAE)
     Estimate maximal u-error and mean absolute error (MAE) for GENERATOR by
     means of a (quasi-) Monte-Carlo simulation with sample size SAMPLESIZE.
     The results are stored in MAX_ERROR and MAE, respectively.

     It returns `UNUR_SUCCESS' if successful.

5.3.6 HRB  -  Hazard Rate Bounded
---------------------------------

Required:
     bounded hazard rate

Optional:
     upper bound for hazard rate

Speed:
     Set-up: fast, Sampling: slow

Reinit:
     supported

Reference:
     [HLD04: Sect.9.1.4; Alg.9.4]



   Generates random variate with given hazard rate which must be bounded from
above. It uses the thinning method with a constant dominating hazard function.

How To Use
..........

HRB requires a hazard function for a continuous distribution together with an
upper bound. The latter has to be set using the `unur_hrb_set_upperbound' call.
If no such upper bound is given it is assumed that the upper bound can be
achieved by evaluating the hazard rate at the left hand boundary of the domain
of the distribution. Notice, however, that for decreasing hazard rate the
method HRD (*note Hazard Rate Decreasing: HRD.) is much faster and thus the
prefered method.

   It is important to note that the domain of the distribution can be set via a
`unur_distr_cont_set_domain' call.  However, the left border must not be
negative. Otherwise it is set to `0'. This is also the default if no domain is
given at all. For computational reasons the right border is always set to
`UNUR_INFINITY' independently of the given domain. Thus for domains bounded
from right the function for computing the hazard rate should return
`UNUR_INFINITY' right of this domain.

   For distributions with increasing hazard rate method HRI (*note Hazard Rate
Increasing: HRI.) is required.

   It is possible to change the parameters and the domain of the chosen
distribution and run `unur_reinit' to reinitialize the generator object.
Notice, that the upper bound given by the `unur_hrb_set_upperbound' call cannot
be changed and must be valid for the changed distribution.

Function reference
------------------

 -- Function: UNUR_PAR* unur_hrb_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_hrb_set_upperbound (UNUR_PAR* PARAMETERS, double
          UPPERBOUND)
     Set upper bound for hazard rate. If this call is not used it is assumed
     that the the maximum of the hazard rate is achieved at the left hand
     boundary of the domain of the distribution.

 -- Function: int unur_hrb_set_verify (UNUR_PAR* PARAMETERS, int VERIFY)
 -- Function: int unur_hrb_chg_verify (UNUR_GEN* GENERATOR, int VERIFY)
     Turn verifying of algorithm while sampling on/off.  If the hazard rate is
     not bounded by the given bound, then `unur_errno' is set to
     `UNUR_ERR_GEN_CONDITION'.

     Default is `FALSE'.

5.3.7 HRD  -  Hazard Rate Decreasing
------------------------------------

Required:
     decreasing (non-increasing) hazard rate

Speed:
     Set-up: fast, Sampling: slow

Reinit:
     supported

Reference:
     [HLD04: Sect.9.1.5; Alg.9.5]



   Generates random variate with given non-increasing hazard rate.  It is
necessary that the distribution object contains this hazard rate. Decreasing
hazard rate implies that the corresponding PDF of the distribution has heavier
tails than the exponential distribution (which has constant hazard rate).

How To Use
..........

HRD requires a hazard function for a continuous distribution with
non-increasing hazard rate. There are no parameters for this method.

   It is important to note that the domain of the distribution can be set via a
`unur_distr_cont_set_domain' call. However, only the left hand boundary is
used. For computational reasons the right hand boundary is always reset to
`UNUR_INFINITY'.  If no domain is given by the user then the left hand boundary
is set to `0'.

   For distributions which do not have decreasing hazard rates but are bounded
from above use method HRB (*note Hazard Rate Bounded: HRB.).  For distributions
with increasing hazard rate method HRI (*note Hazard Rate Increasing: HRI.) is
required.

   It is possible to change the parameters and the domain of the chosen
distribution and run `unur_reinit' to reinitialize the generator object.

Function reference
------------------

 -- Function: UNUR_PAR* unur_hrd_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_hrd_set_verify (UNUR_PAR* PARAMETERS, int VERIFY)
 -- Function: int unur_hrd_chg_verify (UNUR_GEN* GENERATOR, int VERIFY)
     Turn verifying of algorithm while sampling on/off.  If the hazard rate is
     not bounded by the given bound, then `unur_errno' is set to
     `UNUR_ERR_GEN_CONDITION'.

     Default is `FALSE'.

5.3.8 HRI  -  Hazard Rate Increasing
------------------------------------

Required:
     increasing (non-decreasing) hazard rate

Speed:
     Set-up: fast, Sampling: slow

Reinit:
     supported

Reference:
     [HLD04: Sect.9.1.6; Alg.9.6]



   Generates random variate with given non-increasing hazard rate.  It is
necessary that the distribution object contains this hazard rate.  Increasing
hazard rate implies that the corresponding PDF of the distribution has heavier
tails than the exponential distribution (which has constant hazard rate).

   The method uses a decomposition of the hazard rate into a main part which is
constant for all x beyond some point p0 and a remaining part. From both of
these parts points are sampled using the thinning method and the minimum of
both is returned. Sampling from the first part is easier as we have a constant
dominating hazard rate. Thus p0 should be large. On the other hand, if p0 is
large than the thinning algorithm needs many iteration. Thus the performance of
the the algorithm deponds on the choice of p0. We found that values close to
the expectation of the generated distribution result in good performance.

How To Use
..........

HRI requires a hazard function for a continuous distribution with
non-decreasing hazard rate.  The parameter p0 should be set to a value close to
the expectation of the required distribution using `unur_hri_set_p0'.  If
performance is crucial one may try other values as well.

   It is important to note that the domain of the distribution can be set via a
`unur_distr_cont_set_domain' call. However, only the left hand boundary is
used. For computational reasons the right hand boundary is always reset to
`UNUR_INFINITY'.  If no domain is given by the user then the left hand boundary
is set to `0'.

   For distributions with decreasing hazard rate method HRD (*note Hazard Rate
Decreasing: HRI.) is required.  For distributions which do not have increasing
or decreasing hazard rates but are bounded from above use method HRB (*note
Hazard Rate Bounded: HRB.).

   It is possible to change the parameters and the domain of the chosen
distribution and run `unur_reinit' to reinitialize the generator object.

   Notice, that the upper bound given by the `unur_hrb_set_upperbound' call
cannot be changed and must be valid for the changed distribution.  Notice that
the parameter p0 which has been set by a `unur_hri_set_p0' call cannot be
changed and must be valid for the changed distribution.

Function reference
------------------

 -- Function: UNUR_PAR* unur_hri_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_hri_set_p0 (UNUR_PAR* PARAMETERS, double P0)
     Set design point for algorithm. It is used to split the domain of the
     distribution. Values for P0 close to the expectation of the distribution
     results in a relatively good performance of the algorithm.  It is
     important that the hazard rate at this point must be greater than `0' and
     less than `UNUR_INFINITY'.

     Default: left boundary of domain + `1.'

 -- Function: int unur_hri_set_verify (UNUR_PAR* PARAMETERS, int VERIFY)
 -- Function: int unur_hri_chg_verify (UNUR_GEN* GENERATOR, int VERIFY)
     Turn verifying of algorithm while sampling on/off.  If the hazard rate is
     not bounded by the given bound, then `unur_errno' is set to
     `UNUR_ERR_GEN_CONDITION'.

     Default is `FALSE'.

5.3.9 ITDR  -  Inverse Transformed Density Rejection
----------------------------------------------------

Required:
     monotone PDF, dPDF, pole

Optional:
     splitting point between pole and tail region, c-values

Speed:
     Set-up: moderate, Sampling: moderate

Reinit:
     supported

Reference:
     [HLDa07]



   ITDR is an acceptance/rejection method that works for monotone densities. It
is especially designed for PDFs with a single pole. It uses different hat
functions for the pole region and for the tail region. For the tail region
_Transformed Density Rejection_ with a single construction point is used.  For
the pole region a variant called _Inverse Transformed Density Rejection_ is
used. The optimal splitting point between the two regions and the respective
maximum local concavity and inverse local concavity (*note Glossary::) that
guarantee valid hat functions for each regions are estimated.  This splitting
point is set to the intersection point of local concavity and inverse local
concavity.  However, it is assumed that both, the local concavity and the
inverse local concavity do not have a local minimum in the interior of the
domain (which is the case for all standard distributions with a single pole).
In other cases (or when the built-in search routines do not compute non-optimal
values) one can provide the splitting point, and the c-values.

How To Use
..........

Method ITDR requires a distribution object with given PDF and its derivative
and the location of the pole (or mode).  The PDF must be monotone and may
contain a pole.  It must be set via the `unur_distr_cont_set_pdf' and
`unur_distr_cont_set_dpdf' calls. The PDF should return UNUR_INFINITY for the
pole. Alternatively, one can also set the logarithm of the PDF and its
derivative via the `unur_distr_cont_set_logpdf' and
`unur_distr_cont_set_dlogpdf' calls. This is in especially useful since then
the setup and search routines are numerically more stable. Moreover, for many
distributions computing the logarithm of the PDF is less expensive then
computing the PDF directly.

   The pole of the distribution is given by a `unur_distr_cont_set_mode' call.
Notice that distributions with "heavy" poles may have numerical problems caused
by the resultion of the floating point numbers used by computers.  While the
minimal distance between two different floating point numbers is about
`1.e-320' near `0.' it increases to `1.e-16' near `1.' Thus any random variate
generator implemented on a digital computer in fact draws samples from a
discrete distribution that approximates the desired continuous distribution.
For distributions with "heavy" poles not at 0 this approximation may be too
crude and thus every goodness-of-fit test will fail.  Besides this theoretic
problem that cannot be resolved we have to take into consideration that
round-off errors occur more frequently when we have PDFs with poles far away
from `0.' Method ITDR tries to handles this situation as good as possible by
moving the pole into `0.' Thus do not use a wrapper for your PDF that hides
this shift since the information about the resolution of the floating point
numbers near the pole gets lost.

   Method ITDR uses different hats for the pole region and for the tail region.
The splitting point between these two regions, the optimal c-value and design
points for constructing the hats using Transformed Density Rejection are
computed automatically.  (The results of these computations can be read using
the respective calls `unur_itdr_get_xi', `unur_itdr_get_cp' , and
`unur_itdr_get_ct' for the intersection point between local concavity and
inverse local concavity, the c-value for the pole and the tail region.)
However, one can also analyze the local concavity and inverse local concavity
set the corresponding values using `unur_itdr_set_xi', `unur_itdr_set_cp' , and
`unur_itdr_set_ct' calls.  Notice, that c-values greater than -1/2 can be set to
`-0.5'. Although this results in smaller acceptance probabities sampling from
the hat distribution is much faster than for other values of c. Depending on
the expenses of evaluating the PDF the resulting algorithm is usually faster.

   It is possible to change the parameters and the domain of the chosen
distribution and run `unur_reinit' to reinitialize the generator object.
However, the values given by `unur_itdr_set_xi', `unur_itdr_set_cp' , or
`unur_itdr_set_ct' calls are then ignored when `unur_reinit' is called.

Function reference
------------------

 -- Function: UNUR_PAR* unur_itdr_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_itdr_set_xi (UNUR_PAR* PARAMETERS, double XI)
     Sets points where local concavity and inverse local concavity are (almost)
     equal. It is used to estimate the respective c-values for pole region and
     hat regions and to determine the splitting point bx between pole and tail
     region.  If no such point is provided it will be computed automatically.

     Default: not set.

 -- Function: int unur_itdr_set_cp (UNUR_PAR* PARAMETERS, double CP)
     Sets parameter CP for transformation T for inverse density in pole region.
     It must be at most 0 and greater than -1.  A value of `-0.5' is treated
     separately and usually results in faster marginal generation time (at the
     expense of smaller acceptance probabilities.  If no CP-value is given it
     is estimated automatically.

     Default: not set.

 -- Function: int unur_itdr_set_ct (UNUR_PAR* PARAMETERS, double CT)
     Sets parameter CT for transformation T for density in tail region.  It
     must be at most 0. For densities with unbounded domain it must be greater
     than -1.  A value of `-0.5' is treated separately and usually results in
     faster marginal generation time (at the expense of smaller acceptance
     probabilities.  If no CT-value is given it is estimated automatically.

     Default: not set.

 -- Function: double unur_itdr_get_xi (UNUR_GEN* GENERATOR)
 -- Function: double unur_itdr_get_cp (UNUR_GEN* GENERATOR)
 -- Function: double unur_itdr_get_ct (UNUR_GEN* GENERATOR)
     Get intersection point XI, and c-values CP and CT, respectively.  (In case
     of an error `UNUR_INFINITY' is returned.)

 -- Function: double unur_itdr_get_area (UNUR_GEN* GENERATOR)
     Get area below hat.  (In case of an error `UNUR_INFINITY' is returned.)

 -- Function: int unur_itdr_set_verify (UNUR_PAR* PARAMETERS, int VERIFY)
     Turn verifying of algorithm while sampling on/off.

     If the condition PDF(x) <= hat(x) is violated for some x then `unur_errno'
     is set to `UNUR_ERR_GEN_CONDITION'. However, notice that this might happen
     due to round-off errors for a few values of x (less than 1%).

     Default is `FALSE'.

 -- Function: int unur_itdr_chg_verify (UNUR_GEN* GENERATOR, int VERIFY)
     Change the verifying of algorithm while sampling on/off.

5.3.10 NINV  -  Numerical INVersion
-----------------------------------

Required:
     CDF

Optional:
     PDF

Speed:
     Set-up: optional, Sampling: (very) slow

Reinit:
     supported


   NINV implementations of some methods for numerical inversion: Newton's
method, regula falsi (combined with interval bisectioning), and bisection
method.  Regula falsi and bisection method require only the CDF while Newton's
method also requires the PDF.  To speed up marginal generation times a table
with suitable starting points can be created during the setup.  The performance
of the algorithm can adjusted by the desired accuracy of the method.  It is
possible to use this method for generating from truncated distributions. The
truncated domain can be changed for an existing generator object.

How To Use
..........

Method NINV generates random variates by numerical inversion and requires a
continuous univariate distribution objects with given CDF. Three variants are
available:

   - Regula falsi  [default]

   - Newton's method

   - Interval bisectioning

   Newton's method additionally requires the PDF of the distribution and cannot
be used otherwise (NINV automatically switches to regula falsi then).  Default
algorithm is regula falsi. It is slightly slower but numerically much more
stable than Newton's algorithm.  Interval bisectioning is the slowest method
and should only be considered as a last resort when the other methods fails.

   It is possible to draw samples from truncated distributions.  The truncated
domain can even be changed for an existing generator object by an
`unur_ninv_chg_truncated' call.

   Marginal generation times can be sped up by means of a table with suitable
starting points which can be created during the setup. Using such a table can
be switched on by means of a `unur_ninv_set_table' call where the table size is
given as a parameter. The table is still useful when the (truncated) domain is
changed often, since it is computed for the domain of the given distribution.
(It is not possible to enlarge this domain.) If it is necessary to recalculate
the table during sampling, the command `unur_ninv_chg_table' can be used.  As a
rule of thumb using such a table is appropriate when the number of generated
points exceeds the table size by a factor of 100.

   The default number of iterations of NINV should be enough for all reasonable
cases. Nevertheless, it is possible to adjust the maximal number of iterations
with the commands `unur_ninv_set_max_iter' and `unur_ninv_chg_max_iter'.  In
particular this might be necessary when the PDF has a pole or the distribution
has extremely heavy tails.

   It is also possible to set/change the accuracy of the method (which also
heavily influencies the generation time).  We use two measures for the
approximation error which can be used independently: x-error and u-error (*note
Inversion:: for more details).  It is possible to set the maximal tolerated
error using with `unur_ninv_set_x_resolution' and with
`unur_ninv_set_u_resolution', resp., and change it with the respective calls
`unur_ninv_chg_x_resolution' and `unur_ninv_chg_x_resolution'.  The algorithm
tries to satisfy _both_ accuracy goals (and raises an error flag it this fails).
One of these accuracy checks can be disabled by setting the accuracy goal to a
negative value.

   NINV tries to use proper starting values for both the regula falsi and
bisection method, and for Newton's method. Of course the user might have more
knowledge about the properties of the target distribution and is able to share
his wisdom with NINV using the respective commands `unur_ninv_set_start' and
`unur_ninv_chg_start'.  It is possible to change the parameters and the domain
of the chosen distribution and run `unur_reinit' to reinitialize the generator
object.  The values given by the last `unur_ninv_chg_truncated' call will be
then changed to the values of the domain of the underlying distribution object.
It is important to note that for a distribution from the UNU.RAN library of
standard distributions (*note Standard distributions: Stddist.)  the
normalization constant has to be updated using the `unur_distr_cont_upd_pdfarea'
call whenever its parameters have been changed by means of a
`unur_distr_cont_set_pdfparams' call.

   It might happen that NINV aborts `unur_sample_cont' without computing the
correct value (because the maximal number iterations has been exceeded). Then
the last approximate value for x is returned (with might be fairly false) and
`unur_error' is set to `UNUR_ERR_GEN_SAMPLING'.

Function reference
------------------

 -- Function: UNUR_PAR* unur_ninv_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_ninv_set_useregula (UNUR_PAR* PARAMETERS)
     Switch to regula falsi combined with interval bisectioning.  (This the
     default.)

 -- Function: int unur_ninv_set_usenewton (UNUR_PAR* PARAMETERS)
     Switch to Newton's method.  Notice that it is numerically less stable than
     regula falsi.  It it is not possible to invert the CDF for a particular
     uniform random number U when calling `unur_sample_cont', `unur_error' is
     set to `UNUR_ERR_GEN_SAMPLING'.  Thus it is recommended to check
     `unur_error' before using the result of the sampling routine.

 -- Function: int unur_ninv_set_usebisect (UNUR_PAR* PARAMETERS)
     Switch to bisection method. This is a slow algorithm and should only be
     used as a last resort.

 -- Function: int unur_ninv_set_max_iter (UNUR_PAR* PARAMETERS, int MAX_ITER)
 -- Function: int unur_ninv_chg_max_iter (UNUR_GEN* GENERATOR, int MAX_ITER)
     Set and change number of maximal iterations.  Default is `100'.

 -- Function: int unur_ninv_set_x_resolution (UNUR_PAR* PARAMETERS, double
          X_RESOLUTION)
 -- Function: int unur_ninv_chg_x_resolution (UNUR_GEN* GENERATOR, double
          X_RESOLUTION)
     Set and change the maximal tolerated relative x-error.  If X_RESOLUTION is
     negative then checking of the x-error is disabled.

     Default is `1.e-8'.

 -- Function: int unur_ninv_set_u_resolution (UNUR_PAR* PARAMETERS, double
          U_RESOLUTION)
 -- Function: int unur_ninv_chg_u_resolution (UNUR_GEN* GENERATOR, double
          U_RESOLUTION)
     Set and change the maximal tolerated (abolute) u-error.  If U_RESOLUTION
     is negative then checking of the u-error is disabled.

     Default is `-1' (disabled).

 -- Function: int unur_ninv_set_start (UNUR_PAR* PARAMETERS, double LEFT,
          double RIGHT)
     Set starting points.  If not set, suitable values are chosen automatically.

     Newton:         LEFT:           starting point
     Regula falsi:   LEFT, RIGHT:    boundary of starting interval

     If the starting points are not set then the follwing points are used by
     default:
     Newton:         LEFT:           CDF(LEFT) = 0.5
     Regula falsi:   LEFT:           CDF(LEFT) = 0.1
                     RIGHT:          CDF(RIGHT) = 0.9

     If LEFT == RIGHT, then  UNU.RAN always uses the default starting points!

 -- Function: int unur_ninv_chg_start (UNUR_GEN* GEN, double LEFT, double RIGHT)
     Change the starting points for numerical inversion.  If left==right, then
     UNU.RAN uses the default starting points (see `unur_ninv_set_start' ).

 -- Function: int unur_ninv_set_table (UNUR_PAR* PARAMETERS, int NO_OF_POINTS)
     Generates a table with NO_OF_POINTS points containing suitable starting
     values for the iteration. The value of NO_OF_POINTS must be at least 10
     (otherwise it will be set to 10 automatically).

     The table points are chosen such that the CDF at these points form an
     equidistance sequence in the interval (0,1).

     If a table is used, then the starting points given by `unur_ninv_set_start'
     are ignored.

     No table is used by default.

 -- Function: int unur_ninv_chg_table (UNUR_GEN* GEN, int NO_OF_POINTS)
     Recomputes a table as described in `unur_ninv_set_table'.

 -- Function: int unur_ninv_chg_truncated (UNUR_GEN* GEN, double LEFT, double
          RIGHT)
     Changes the borders of the domain of the (truncated) distribution.

     Notice that the given truncated domain must be a subset of the domain of
     the given distribution. The generator always uses the intersection of the
     domain of the distribution and the truncated domain given by this call.
     Moreover the starting point(s) will not be changed.

     _Important:_ If the CDF is (almost) the same for LEFT and RIGHT and
     (almost) equal to `0' or `1', then the truncated domain is _not_ chanced
     and the call returns an error code.

     _Notice:_ If the parameters of the distribution has been changed by a
     `unur_distr_cont_set_pdfparams' call it is recommended to set the
     truncated domain again, since the former call might change the domain of
     the distribution but not update the values for the boundaries of the
     truncated distribution.

 -- Function: double unur_ninv_eval_approxinvcdf (const UNUR_GEN* GENERATOR,
          double U)
     Get approximate approximate value of inverse CDF at U.  If U is out of the
     domain [0,1] then `unur_errno' is set to `UNUR_ERR_DOMAIN' and the
     respective bound of the domain of the distribution are returned (which is
     `-UNUR_INFINITY' or `UNUR_INFINITY' in the case of unbounded domains).

     _Notice_: This function always evaluates the inverse CDF of the given
     distribution. A call to `unur_ninv_chg_truncated' call has no effect.

5.3.11 NROU  -  Naive Ratio-Of-Uniforms method
----------------------------------------------

Required:
     PDF

Optional:
     mode, center, bounding rectangle for acceptance region

Speed:
     Set-up: slow or fast, Sampling: moderate

Reinit:
     supported

Reference:
     [HLD04: Sect.2.4 and Sect.6.4]



   NROU is an implementation of the (generalized) ratio-of-uniforms method
which uses (minimal) bounding rectangles, see *note Ratio-of-Uniforms::. It
uses a positive control parameter r for adjusting the algorithm to the given
distribution to improve performance and/or to make this method applicable.
Larger values of r increase the class of distributions for which the method
works at the expense of a higher rejection constant. For computational reasons
r=1 should be used if possible (this is the default).  Moreover, this
implementation uses the center mu of the distribution (see
`unur_distr_cont_get_center' for details of its default values).

   For the special case with r=1 the coordinates of the minimal bounding
rectangles are given by

     v^+ = sup_(x) sqrt(PDF(x)),
     u^- = inf_(x) (x- mu) sqrt(PDF(x)),
     u^+ = sup_(x) (x- mu) sqrt(PDF(x)),

where mu is the center of the distribution.  For other values of r we have

     v^+ = sup_(x) (PDF(x))^(1/(r+1)),
     u^- = inf_(x) (x- mu) (PDF(x))^(r/(r+1)),
     u^+ = sup_(x) (x- mu) (PDF(x))^(r/(r+1)).

These bounds can be given directly. Otherwise they are computed automatically
by means of a (slow) numerical routine.  Of course this routine can fail,
especially when this rectangle is not bounded.

   It is important to note that the algorithm works with PDF(x- mu) instead of
PDF(x).  This is important as otherwise the acceptance region can become a very
long and skinny ellipsoid along a diagonal of the (huge) bounding rectangle.

How To Use
..........

For using the NROU method UNU.RAN needs the PDF of the distribution.
Additionally, the parameter r can be set via a `unur_vnrou_set_r' call. Notice
that the acceptance probability decreases when r is increased. On the other
hand is is more unlikely that the bounding rectangle does not exist if r is
small.

   A bounding rectangle can be given by the `unur_vnrou_set_u' and
`unur_vnrou_set_v' calls.

   _Important:_ The bounding rectangle has to be provided for the function
PDF(x-center)!  Notice that `center' is the center of the given distribution,
see `unur_distr_cont_set_center'.  If in doubt or if this value is not optimal,
it can be changed (overridden) by a `unur_nrou_set_center' call.

   If the coordinates of the bounding rectangle are not provided by the user
then the minimal bounding rectangle is computed automatically.

   By means of `unur_vnrou_set_verify' and `unur_vnrou_chg_verify' one can run
the sampling algorithm in a checking mode, i.e., in every cycle of the
rejection loop it is checked whether the used rectangle indeed enclosed the
acceptance region of the distribution. When in doubt (e.g., when it is not
clear whether the numerical routine has worked correctly) this can be used to
run a small Monte Carlo study.

   It is possible to change the parameters and the domain of the chosen
distribution and run `unur_reinit' to reinitialize the generator object.
Notice, that derived parameters like the mode must also be (re-) set if the
parameters or the domain has be changed.  Notice, however, that then the values
that has been set by `unur_vnrou_set_u' and `unur_vnrou_set_v' calls are
removed and the coordinates of the bounding box are computed numerically.

Function reference
------------------

 -- Function: UNUR_PAR* unur_nrou_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_nrou_set_u (UNUR_PAR* PARAMETERS, double UMIN, double
          UMAX)
     Sets left and right boundary of bounding rectangle.  If no values are
     given, the boundary of the minimal bounding rectangle is computed
     numerically.

     _Notice_: Computing the minimal bounding rectangle may fail under some
     circumstances. Moreover, for multimodal distributions the bounds might be
     too small as only local extrema are computed.  Nevertheless, for T_c
     -concave distributions with c=-1/2 it should work.

     _Important:_ The bounding rectangle that has to be provided is for the
     function PDF(x-center)!

     Default: not set.

 -- Function: int unur_nrou_set_v (UNUR_PAR* PARAMETERS, double VMAX)
     Set upper boundary for bounding rectangle. If this value is not given then
     sqrt(PDF(mode)) is used instead.

     _Notice_: When the mode is not given for the distribution object, then it
     will be computed numerically.

     Default: not set.

 -- Function: int unur_nrou_set_r (UNUR_PAR* PARAMETERS, double R)
     Sets the parameter R of the generalized ratio-of-uniforms method.

     _Notice_: This parameter must satisfy R>0.

     Default: `1'.

 -- Function: int unur_nrou_set_center (UNUR_PAR* PARAMETERS, double CENTER)
     Set the center (mu ) of the PDF.  If not set the center of the given
     distribution object is used.

     Default: see `unur_distr_cont_set_center'.

 -- Function: int unur_nrou_set_verify (UNUR_PAR* PARAMETERS, int VERIFY)
     Turn verifying of algorithm while sampling on/off.

     If the condition PDF(x) <= hat(x) is violated for some x then `unur_errno'
     is set to `UNUR_ERR_GEN_CONDITION'. However, notice that this might happen
     due to round-off errors for a few values of x (less than 1%).

     Default is `FALSE'.

 -- Function: int unur_nrou_chg_verify (UNUR_GEN* GENERATOR, int VERIFY)
     Change the verifying of algorithm while sampling on/off.

5.3.12 PINV  -  Polynomial interpolation based INVersion of CDF
---------------------------------------------------------------

Required:
     PDF

Optional:
     domain, center, CDF, derivative of PDF

Speed:
     Set-up: (very) slow, Sampling: (very) fast

Reinit:
     not implemented

Reference:
     [DHLa08]



   PINV is a variant of numerical inversion, where the inverse CDF is
approximated using Newton's interpolating formula.  The interval [0,1] is split
into several subintervals. In each of these the inverse CDF is constructed at
nodes (CDF(x),x) for some points x in this subinterval.  If the PDF is given,
then the CDF is computed numerically from the given PDF using adaptive
Gauss-Lobatto integration with 5 points. Subintervals are split until the
requested accuracy goal is reached.

   The method is not exact, as it only produces random variates of the
approximated distribution. Nevertheless, the maximal tolerated approximation
error can be set to be the resolution (but of course is bounded by the machine
precision).  We use the u-error |U-CDF(X)| to measure the error for X =
"approximate inverse CDF"(U).  Notice that very small values of the
u-resolution are possible but increase the cost for the setup step.  We call
the maximal tolerated u-error the _u-resolution_ of the algorithm in the sequel.

   Both the order of the interpolating polynomial and the u-resolution can be
selected.

   The interpolating polynomials have to be computed in a setup step. However,
it only works for distributions with bounded domain; for distributions with
unbounded domain the tails are cut off such that the probability for the tail
regions is small compared to the given u-resolution.

   The construction of the interpolation polynomial only works when the PDF is
unimodal or when the PDF does not vanish between two modes.

   There are some restrictions for the given distribution:
   * The support of the distribution (i.e., the region where the PDF is
     strictly positive) must be connected. In practice this means, that the
     region where PDF is "not too small" must be connected.  Unimodal densities
     satisfy this condition.  If this condition is violated then the domain of
     the distribution might be truncated.

   * When the PDF is integrated numerically, then the given PDF must be
     continuous and should be smooth.

   * The PDF must be bounded.

   * The algorithm has problems when the distribution has heavy tails (as then
     the inverse CDF becomes very steep at 0 or 1) and the requested
     u-resolution is very small.  E.g., the Cauchy distribution is likely to
     show this problem when the requested u-resolution is less then `1.e-12'.
   Regions with very small PDF values or heavy tails might lead to an abortion
of the set-up or (even worse) the approximation error might become larger than
requested, since the (computation of the) interpolating polynomial becomes
numerically unstable.

   _Remark:_ We also have implemented experimental variants.  However, we
observed that these variants are more sensitive to round-off errors, especially
in the right hand tail and we _do not recommend_ their usage unless there are
severe reasons.

   - Use a function that implements the CDF instead of numerical integration of
     the PDF.

   - Use Hermite interpolation instead of Newton interpolation.  Thus the first
     (and second) derivative of the interpolating polynomial coincides with
     that of the inverse CDF.  Consequently the interpolant is also (twice)
     differentiable even at the interval boundaries.  This variant can be seen
     as limiting case of Newton interpolation with double (or triple) points as
     nodes.

     We have used a _smoothness_ parameter to control this feature. However,
     besides numerical problems we observed that this variant requires more
     intervals and thus larger setup times and higher memory consumptions.

How To Use
..........

PINV works for continuous univariate distribution objects with given PDF. The
corresponding distribution object should contain a typical point of the
distribution, i.e., a point where the PDF is not too small, e.g., (a point
near) the mode.  However, it is important that the center is *not* the pole of
the distribution (or a point too close to the pole).  It can be set using a
`unur_distr_cont_set_center' or a `unur_distr_cont_set_mode' call. If neither
is set, or if the given center cannot be used, then a simple search routine
tries to find an appropriate point for the center.

   It is recommended that the domain of the distribution with bounded domain is
specified using a `unur_distr_cont_set_domain' call. Otherwise, the boundary is
searched numerically which might be rather expensive, especially when this
boundary point is `0'.

   When sampling from truncated distributions with extreme truncation points,
it is recommended to provide the log-density using `unur_distr_cont_set_logpdf'
and the mode.  Then the PDF is rescaled such that the PDF at the mode is 1.
Thus the algorithm is numerically more stable.

   The inverse CDF is interpolated using Newton polynomials.  The order of this
polynomial can be set by means of a `unur_pinv_set_order' call.

   The smoothness of the interpolant at interval boundaries can be controlled
using a `unur_pinv_set_smoothness' call.  Then Hermite interpolation instead of
Newton interpolation is used. (The former can be seen as a limiting case of
Newton interpolation with double (or triple) points.)  However, using higher
smoothness is _not recommended_ unless differentiability at the interval
boundaries is important.

   For distributions with unbounded domains the tails are cut off such that the
probability for the tail regions is small compared to the given u-resolution.
For finding these cut points the algorithm starts with the region
`[-1.e100,1.e100]'. For the exceptional case where this does not work these
starting points can be changed via a `unur_pinv_set_boundary' call.

   This method is not exact, as it only produces random variates of the
approximated distribution. Nevertheless, the numerical error in "u-direction"
(i.e., |U-CDF(X)|, for X = "approximate inverse CDF"(U) |U-CDF(X)|) can be
controlled by means of `unur_pinv_set_u_resolution'.  However, the maximal
error of this approximation is only estimated. For very small u-resolutions the
actual approximation error might be (slightly) larger than the requested
u-resolution.  (Of course the size of this value depends on the given PDF.)  If
this error is crucial for an application we recommend to compute this error
using `unur_pinv_estimate_error' which runs a small Monte Carlo simulation.
See also the documentation for function `unur_pinv_set_u_resolution' and the
remark given there.

   The number of required subintervals heavily depends on the order of the
interpolating polynomial and the requested u-resolution: it increases when
order or u-resolution are decreased.  It can be checked using a
`unur_pinv_get_n_intervals' call.  The maximum number of such subintervals is
fixed but can be increased using a `unur_pinv_set_max_intervals' call.  If this
maximum number is too small then the set-up aborts with a corresponding error
message.

   It is also possible to use the CDF of the distribution instead of the PDF.
Then the distribution object must contain a pointer to the CDF. Moreover, this
variant of the algorithm has to be switched on using an `unur_pinv_set_usecdf'
call.  Notice, however, that the setup for this variant is numerically less
stable than using integration of the PDF (the default variant). Thus using the
CDF is _not recommended_.

Function reference
------------------

 -- Function: UNUR_PAR* unur_pinv_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_pinv_set_order (UNUR_PAR* PARAMETERS, int ORDER)
     Set order of interpolation. Valid orders are between `3' and `17'. Higher
     orders result in fewer intervals for the approximations.

     Default: `5'.

 -- Function: int unur_pinv_set_smoothness (UNUR_PAR* PARAMETERS, int
          SMOOTHNESS)
     Set smoothness of interpolant. By construction the interpolant is
     piecewise polynomial and thus smooth on each of the intervals where these
     polynomials are constructed. At the interval boundaries, however, it
     usually not be differentiable.  Method PINV also implements variants of
     Newton interpolation where the first (or second) derivative of the
     interpolating polynomial coincides with the respective derivative of the
     inverse CDF at the nodes. The the interpolant is (twice) differentiable
     even at the interval boundaries.  These variants can be seen as limiting
     case of Newton interpolation with double (or triple) points as nodes and
     are known as Hermite interpolation.

     Possible values for SMOOTHNESS:

     Value    Effect              Requirements
     --------------------------------------------------------------------------- 
     `0'      continuous          requires PDF (or CDF)
     `1'      differentiable      requires PDF (optional: CDF),
                                  order of polynomial must be odd
     `2'      twice               requires PDF and its derivative (optional:
              differentiable      CDF),
                                  order must be 5, 8, 11, 14 or 17

     If the order of the polynomial does not satisfy the given condition, then
     it is increased to the next larger possible value.

     _Remark:_ A higher smoothness parameter usually results in a higher number
     of intervals and thus a higher setup time and memory consumption.  We also
     observed that higher smoothness parameters make the algorithm more
     sensible for round-off error. Then the setup fails.

     _Remark:_ If the interpolating polynomial cannot be constructed for the
     requested smoothness on a particular interval, then the smoothness
     parameter is reduced for that interval.

     _Remark:_ For order `3' and smoothness `1' (cubic Hermite interpolation)
     monotonicity is guaranteed by checking a simple monotonicity condition for
     the coefficients of the polynomials.

     _Remark:_ Using SMOOTHNESS larger than `0' is _not recommended_ unless
     differentiability at the interval boundaries is important for ones
     application.

     Default: `0'.

 -- Function: int unur_pinv_set_u_resolution (UNUR_PAR* PARAMETERS, double
          U_RESOLUTION)
     Set maximal tolerated u-error. Values of U_RESOLUTION must at least
     `1.e-15' and `1.e-5' at most.  Notice that the resolution of most uniform
     random number sources is 2^(-32) = `2.3e-10'. Thus a value of `1.e-10'
     leads to an inversion algorithm that could be called exact. For most
     simulations slightly bigger values for the maximal error are enough as
     well.

     Smaller values for U_RESOLUTION increase the number of subinterval that
     are necessary for the approximation of the inverse CDF. For very small
     values (less then `1.e-12') this number might exceed the maximum number of
     such intervals. However, this number can be increased using a
     `unur_pinv_set_max_intervals' call.

     _Remark:_ We ran many experiments and found that the observed u-error was
     always smaller than the given U_RESOLUTION whenever this value was
     `1.e-12'. For values smaller than `1e-13' the maximal observed u-error was
     slightly larger. One use `1.e-15' if best approximation is required.
     However, then the actual u-error can be as large as `1.e-14'.

     *Warning!* These figures are based on our experiments (with some tolerance
     added to be on the safe side). There is no guarantee for these error
     estimates for a particular distribution.

     Default is `1.e-10'.

 -- Function: int unur_pinv_set_use_upoints (UNUR_PAR* PARAMETERS, int
          USE_UPOINTS)
     If USE_UPOINTS is `TRUE', then the nodes of the interpolating polynomial
     are constructed by means of Chebyshev points in u-scale not in x-scale.
     This results is a better approximation but almost doubles the number of
     PDF or CDF evaluations during the setup.  (This is an experimental
     feature.)

     Default: `FALSE'

 -- Function: int unur_pinv_set_usepdf (UNUR_PAR* PARAMETERS)
     Use PDF (if available) to compute approximate inverse CDF.

     This is the default.

 -- Function: int unur_pinv_set_usecdf (UNUR_PAR* PARAMETERS)
     Use CDF (if available) to compute approximate inverse CDF.  This variant
     is intend for running experiments with method PINV.

     _Remark:_ We ran many experiments and found that for small values of the
     given U_RESOLUTION (less than `1.e-12') the setup fails for distributions
     with heavy tails. We found that using the PDF (instead of the CDF) is
     numerically more stable.  This is especially the case when the smoothness
     parameter is set to `1' or `2'.

     Using the CDF is *not recommended*.

 -- Function: int unur_pinv_set_boundary (UNUR_PAR* PARAMETERS, double LEFT,
          double RIGHT)
     Set LEFT and RIGHT point for finding the cut-off points for the
     "computational domain", i.e., the domain that covers the essential part of
     the distribution.  The cut-off points are computed such that the tail
     probabilities are smaller than given by `unur_pinv_set_u_resolution'.  It
     is usually safe to use a large interval.  However, `+/- UNUR_INFINITY' is
     not allowed.

     _Important_: This call does not change the domain of the given
     distribution itself. But it restricts the domain for the resulting random
     variates.

     Default: intersection of `[-1.e100,+1.e100]' and the given domain of the
     distribution.

 -- Function: int unur_pinv_set_searchboundary (UNUR_PAR* PARAMETERS, int LEFT,
          int RIGHT)
     If LEFT or RIGHT is set to `FALSE' then the respective boundary as given
     by a `unur_pinv_set_boundary' call is used without any further
     computations.  However, these boundary points might cause numerical
     problems during the setup when PDF returns `0' "almost everywhere".  If
     set to `TRUE' (the default) then the computational interval is shortened
     to a more sensible region by means of a search algorithm.  Switching off
     this search is useful, e.g., for the Gamma(2) distribution where the left
     border `0' is fixed and finite.

     _Remark:_ The searching algorithm assumes that the support of the
     distribution is connected.

     _Remark:_ Do not set this parameter to `FALSE' except when searching for
     cut-off points fails and one wants to try with precomputed values.

     Default: `TRUE'.

 -- Function: int unur_pinv_set_max_intervals (UNUR_PAR* PARAMETERS, int
          MAX_IVS)
     Set maximum number of intervals. MAX_IVS must be at least `100' and at
     most `1000000'.

     Default is `10000'.

 -- Function: int unur_pinv_get_n_intervals (const UNUR_GEN* GENERATOR)
     Get number of intervals used for interpolation in the generator object.
     It returns `0' in case of an error.

 -- Function: int unur_pinv_set_keepcdf (UNUR_PAR* PARAMETERS, int KEEPCDF)
     If the PDF is given, then the CDF is computed numerically from the given
     PDF using adaptive Gauss-Lobatto integration.  Thus a table of CDF points
     is stored to keep the number of evaluations of the PDF minimal. Usually
     this table is discarded when the setup is completed.  If KEEPCDF is
     `TRUE', then this table is kept and can be used to compute the CDF of the
     underlying distribution by means of function `unur_pinv_eval_approxcdf'.
     This option is ignored when `unur_pinv_set_usecdf' is called.

     Default: `FALSE'

 -- Function: double unur_pinv_eval_approxinvcdf (const UNUR_GEN* GENERATOR,
          double U)
     Evaluate interpolation of inverse CDF at U.  If U is out of the domain
     (0,1) then `unur_errno' is set to `UNUR_ERR_DOMAIN' and the respective
     bound of the domain of the distribution are returned (which is
     `-UNUR_INFINITY' or `UNUR_INFINITY' in the case of unbounded domains).

 -- Function: double unur_pinv_eval_approxcdf (const UNUR_GEN* GENERATOR,
          double X)
     Evaluate (approximate) CDF at X. If the PDF of the distribution is given,
     then adaptive Gauss-Lobatto integration is used to compute the CDF.  If
     the PDF is used to create the generator object, then the table of integral
     values must not removed at the end of setup and thus
     `unur_pinv_set_keepcdf' must be called.

 -- Function: int unur_pinv_estimate_error (const UNUR_GEN* GENERATOR, int
          SAMPLESIZE, double* MAX_ERROR, double* MAE)
     Estimate maximal u-error and mean absolute error (MAE) for GENERATOR by
     means of Monte-Carlo simulation with sample size SAMPLESIZE.  The results
     are stored in MAX_ERROR and MAE, respectively.

     It returns `UNUR_SUCCESS' if successful.

5.3.13 SROU  -  Simple Ratio-Of-Uniforms method
-----------------------------------------------

Required:
     T-concave PDF, mode, area

Speed:
     Set-up: fast, Sampling: slow

Reinit:
     supported

Reference:
     [LJa01] [LJa02] [HLD04: Sect.6.3.1; Sect.6.3.2; Sect.6.4.1; Alg.6.4;
     Alg.6.5; Alg.6.7]



   SROU is based on the ratio-of-uniforms method (*note Ratio-of-Uniforms::)
that uses universal inequalities for constructing a (universal) bounding
rectangle.  It works for all T-concave distributions, including log-concave and
T-concave distributions with T(x) = -1/sqrt(x).

   Moreover an (optional) parameter `r' can be given, to adjust the generator
to the given distribution. This parameter is strongly related to the parameter
`c' for transformed density rejection (*note TDR::) via the formula c =
-r/(r+1). The rejection constant increases with higher values for `r'. On the
other hand, the given density must be T_c -concave for the corresponding c.
The default setting for `r' is 1 which results in a very simple code. (For
other settings, sampling uniformly from the acceptance region is more
complicated.)

   Optionally the CDF at the mode can be given to increase the performance of
the algorithm. Then the rejection constant is reduced by 1/2 and (if `r=1')
even a universal squeeze can (but need not be) used.  A way to increase the
performance of the algorithm when the CDF at the mode is not provided is the
usage of the mirror principle (only if `r=1'). However, using squeezes and using
the mirror principle is only recommended when the PDF is expensive to compute.

   The exact location of the mode and/or the area below the PDF can be replace
by appropriate bounds. Then the algorithm still works but has larger rejection
constants.

How To Use
..........

SROU works for any continuous univariate distribution object with given T_c
-concave PDF with c<1, ) mode and area below PDF. Optional the CDF at the mode
can be given to increase the performance of the algorithm by means of the
`unur_srou_set_cdfatmode' call. Additionally squeezes can be used and switched
on via `unur_srou_set_usesqueeze' (only if `r=1').  A way to increase the
performance of the algorithm when the CDF at the mode is not provided is the
usage of the mirror principle which can be swithced on by means of a
`unur_srou_set_usemirror' call (only if `r=1') .  However using squeezes and
using the mirror principle is only recommended when the PDF is expensive to
compute.

   The parameter `r' can be given, to adjust the generator to the given
distribution. This parameter is strongly related parameter `c' for transformed
density rejection via the formula c = -r/(r+1).  The parameter `r' can be any
value larger than or equal to 1. Values less then 1 are automatically set to 1.
The rejection constant depends on the chosen parameter `r' but not on the
particular distribution. It is 4 for `r' equal to 1 and higher for higher
values of `r'.  It is important to note that different algorithms for different
values of `r': If `r' equal to 1 this is much faster than the algorithm for `r'
greater than 1.  The default setting for `r' is 1.

   If the (exact) area below the PDF is not known, then an upper bound can be
used instead (which of course increases the rejection constant).  But then the
squeeze flag must not be set and `unur_srou_set_cdfatmode' must not be used.

   If the exact location of the mode is not known, then use the approximate
location and provide the (exact) value of the PDF at the mode by means of the
`unur_srou_set_pdfatmode' call. But then `unur_srou_set_cdfatmode' must not be
used. Notice, that a (slow) numerical mode finder will be used if no mode is
given at all.  It is even possible to give an upper bound for the PDF only.
However, then the (upper bound for the) area below the PDF has to be multiplied
by the ratio between the upper bound and the lower bound of the PDF at the
mode.  Again setting the squeeze flag and using `unur_srou_set_cdfatmode' is
not allowed.

   It is possible to change the parameters and the domain of the chosen
distribution and run `unur_reinit' to reinitialize the generator object.
Notice, that derived parameters like the mode must also be (re-) set if the
parameters or the domain has be changed.  Moreover, if the PDF at the mode has
been provided by a `unur_srou_set_pdfatmode' call, additionally
`unur_srou_chg_pdfatmode' must be used (otherwise this call is not necessary
since then this figure is computed directly from the PDF).

   There exists a test mode that verifies whether the conditions for the method
are satisfied or not while sampling. It can be switched on by calling
`unur_srou_set_verify' and `unur_srou_chg_verify', respectively. Notice however
that sampling is (a little bit) slower then.

Function reference
------------------

 -- Function: UNUR_PAR* unur_srou_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_srou_set_r (UNUR_PAR* PARAMETERS, double R)
     Set parameter R for transformation.  Only values greater than or equal to
     1 are allowed.  The performance of the generator decreases when R is
     increased. On the other hand R must not be set to small, since the given
     density must be T_c-concave for c = -r/(r+1).

     _Notice:_ If R is set to `1' a simpler and much faster algorithm is used
     then for R greater than one.

     For computational reasons values of R that are greater than `1' but less
     than `1.01' are always set to `1.01'.

     Default is `1'.

 -- Function: int unur_srou_set_cdfatmode (UNUR_PAR* PARAMETERS, double FMODE)
     Set CDF at mode.  When set, the performance of the algorithm is increased
     by factor 2.  However, when the parameters of the distribution are changed
     `unur_srou_chg_cdfatmode' has to be used to update this value.

     Default: not set.

 -- Function: int unur_srou_set_pdfatmode (UNUR_PAR* PARAMETERS, double FMODE)
     Set pdf at mode.  When set, the PDF at the mode is never changed.  This is
     to avoid additional computations, when the PDF does not change when
     parameters of the distributions vary.  It is only useful when the PDF at
     the mode does not change with changing parameters of the distribution.

     _IMPORTANT:_ This call has to be executed after a possible call of
     `unur_srou_set_r'.  Default: not set.

 -- Function: int unur_srou_set_usesqueeze (UNUR_PAR* PARAMETERS, int
          USESQUEEZE)
     Set flag for using universal squeeze (default: off).  Using squeezes is
     only useful when the evaluation of the PDF is (extremely) expensive.
     Using squeezes is automatically disabled when the CDF at the mode is not
     given (then no universal squeezes exist).

     Squeezes can only be used if `r=1'.

     Default is `FALSE'.

 -- Function: int unur_srou_set_usemirror (UNUR_PAR* PARAMETERS, int USEMIRROR)
     Set flag for using mirror principle (default: off).  Using the mirror
     principle is only useful when the CDF at the mode is not known and the
     evaluation of the PDF is rather cheap compared to the marginal generation
     time of the underlying uniform random number generator.  It is
     automatically disabled when the CDF at the mode is given.  (Then there is
     no necessity to use the mirror principle. However disabling is only done
     during the initialization step but not at a re-initialization step.)

     The mirror principle can only be used if `r=1'.

     Default is `FALSE'.

 -- Function: int unur_srou_set_verify (UNUR_PAR* PARAMETERS, int VERIFY)
 -- Function: int unur_srou_chg_verify (UNUR_GEN* GENERATOR, int VERIFY)
     Turn verifying of algorithm while sampling on/off.  If the condition
     squeeze(x) <= PDF(x) <= hat(x) is violated for some x then `unur_errno' is
     set to `UNUR_ERR_GEN_CONDITION'. However notice that this might happen due
     to round-off errors for a few values of x (less than 1%).

     Default is `FALSE'.

 -- Function: int unur_srou_chg_cdfatmode (UNUR_GEN* GENERATOR, double FMODE)
     Change CDF at mode of distribution.  `unur_reinit' must be executed before
     sampling from the generator again.

 -- Function: int unur_srou_chg_pdfatmode (UNUR_GEN* GENERATOR, double FMODE)
     Change PDF at mode of distribution.  `unur_reinit' must be executed before
     sampling from the generator again.

5.3.14 SSR  -  Simple Setup Rejection
-------------------------------------

Required:
     T-concave PDF, mode, area

Speed:
     Set-up: fast, Sampling: slow

Reinit:
     supported

Reference:
     [LJa01] [HLD04: Sect.6.3.3; Alg.6.6]



   SSR is an acceptance/rejection method that uses universal inequalities for
constructing (universal) hats and squeezes (*note Rejection::).  It works for
all T-concave distributions with T(x) = -1/sqrt(x).

   It requires the PDF, the (exact) location of the mode and the area below the
given PDF. The rejection constant is 4 for all T-concave distributions with
unbounded domain and is less than 4 when the domain is bounded. Optionally the
CDF at the mode can be given to increase the performance of the algorithm.
Then the rejection constant is at most 2 and a universal squeeze can (but need
not be) used. However, using squeezes is not recommended unless the evaluation
of the PDF is expensive.

   The exact location of the mode and/or the area below the PDF can be replace
by appropriate bounds. Then the algorithm still works but has larger rejection
constants.

How To Use
..........

SSR works for any continuous univariate distribution object with given
T-concave PDF (with T(x) = -1/sqrt(x),) mode and area below PDF. Optional the
CDF at the mode can be given to increase the performance of the algorithm by
means of the `unur_ssr_set_cdfatmode' call. Additionally squeezes can be used
and switched on via `unur_ssr_set_usesqueeze'.  If the (exact) area below the
PDF is not known, then an upper bound can be used instead (which of course
increases the rejection constant).  But then the squeeze flag must not be set
and `unur_ssr_set_cdfatmode' must not be used.

   If the exact location of the mode is not known, then use the approximate
location and provide the (exact) value of the PDF at the mode by means of the
`unur_ssr_set_pdfatmode' call. But then `unur_ssr_set_cdfatmode' must not be
used. Notice, that a (slow) numerical mode finder will be used if no mode is
given at all.  It is even possible to give an upper bound for the PDF only.
However, then the (upper bound for the) area below the PDF has to be multiplied
by the ratio between the upper bound and the lower bound of the PDF at the
mode.  Again setting the squeeze flag and using `unur_ssr_set_cdfatmode' is not
allowed.

   It is possible to change the parameters and the domain of the chosen
distribution and run `unur_reinit' to reinitialize the generator object.
Notice, that derived parameters like the mode must also be (re-) set if the
parameters or the domain has be changed.  Moreover, if the PDF at the mode has
been provided by a `unur_ssr_set_pdfatmode' call, additionally
`unur_ssr_chg_pdfatmode' must be used (otherwise this call is not necessary
since then this figure is computed directly from the PDF).

   _Important:_ If any of mode, PDF or CDF at the mode, or the area below the
mode has been changed, then `unur_reinit' must be executed.  (Otherwise the
generator produces garbage).

   There exists a test mode that verifies whether the conditions for the method
are satisfied or not while sampling. It can be switched on/off by calling
`unur_ssr_set_verify' and `unur_ssr_chg_verify', respectively.  Notice,
however, that sampling is (a little bit) slower then.

Function reference
------------------

 -- Function: UNUR_PAR* unur_ssr_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_ssr_set_cdfatmode (UNUR_PAR* PARAMETERS, double FMODE)
     Set CDF at mode.  When set, the performance of the algorithm is increased
     by factor 2.  However, when the parameters of the distribution are changed
     `unur_ssr_chg_cdfatmode' has to be used to update this value.

     Default: not set.

 -- Function: int unur_ssr_set_pdfatmode (UNUR_PAR* PARAMETERS, double FMODE)
     Set pdf at mode.  When set, the PDF at the mode is never changed.  This is
     to avoid additional computations, when the PDF does not change when
     parameters of the distributions vary.  It is only useful when the PDF at
     the mode does not change with changing parameters for the distribution.

     Default: not set.

 -- Function: int unur_ssr_set_usesqueeze (UNUR_PAR* PARAMETERS, int USESQUEEZE)
     Set flag for using universal squeeze (default: off).  Using squeezes is
     only useful when the evaluation of the PDF is (extremely) expensive.
     Using squeezes is automatically disabled when the CDF at the mode is not
     given (then no universal squeezes exist).

     Default is `FALSE'.

 -- Function: int unur_ssr_set_verify (UNUR_PAR* PARAMETERS, int VERIFY)
 -- Function: int unur_ssr_chg_verify (UNUR_GEN* GENERATOR, int VERIFY)
     Turn verifying of algorithm while sampling on/off.  If the condition
     squeeze(x) <= PDF(x) <= hat(x) is violated for some x then `unur_errno' is
     set to `UNUR_ERR_GEN_CONDITION'. However notice that this might happen due
     to round-off errors for a few values of x (less than 1%).

     Default is `FALSE'.

 -- Function: int unur_ssr_chg_cdfatmode (UNUR_GEN* GENERATOR, double FMODE)
     Change CDF at mode of distribution.  `unur_reinit' must be executed before
     sampling from the generator again.

 -- Function: int unur_ssr_chg_pdfatmode (UNUR_GEN* GENERATOR, double FMODE)
     Change PDF at mode of distribution.  `unur_reinit' must be executed before
     sampling from the generator again.

5.3.15 TABL  -  a TABLe method with piecewise constant hats
-----------------------------------------------------------

Required:
     PDF, all local extrema, cut-off values for the tails

Optional:
     approximate area

Speed:
     Set-up: (very) slow, Sampling: fast

Reinit:
     not implemented

Reference:
     [AJa93] [AJa95] [HLD04: Cha.5.1]



   TABL (called Ahrens method in [HLD04] ) is an acceptance/rejection method
(*note Rejection::) that uses a decomposition of the domain of the distribution
into many short subintervals. Inside of these subintervals constant hat and
squeeze functions are utilized. Thus it is easy to use the idea of immediate
acceptance for points below the squeeze. This reduces the expected number of
uniform random numbers per generated random variate to less than two. Using a
large number of subintervals only little more than one random number is
necessary on average. Thus this method becomes very fast.

   Due to the constant hat function this method only works for distributions
with bounded domains. Thus for unbounded domains the left and right tails have
to be cut off.  This is no problem when the probability of falling into these
tail regions is beyond computational relevance (e.g. smaller than `1.e-12').

   For easy construction of hat and squeeze functions it is necessary to know
the regions of monotonicity (called _slopes_) or equivalently all local maxima
and minima of the density.  The main problem for this method in the setup is
the choice of the subintervals. A simple and close to optimal approach is the
"equal area rule" [HLD04: Cha.5.1] . There the subintervals are selected such
that the area below the hat is the same for each subinterval which can be
realized with a simple recursion.  If more subintervals are necessary it is
possible to split either randomly chosen intervals (adaptive rejection
sampling, ARS) or those intervals, where the ratio between squeeze and hat is
smallest. This version of the setup is called derandomized ARS (DARS). With the
default settings TABL is first calculating approximately 30 subintervals with
the equal area rule. Then DARS is used till the desired fit of the hat is
reached.

   A convenient measure to control the quality of the fit of hat and squeeze is
the ratio (area below squeeze)/(area below hat) called `sqhratio' which must be
smaller or equal to one.  The expected number of iterations in the rejection
algorithm is known to be smaller than 1/sqhratio and the expected number of
evaluations of the density is bounded by `1/sqhratio - 1'.  So values of the
sqhratio close to one (e.g. `0.95' or `0.99') lead to many subintervals. Thus a
better fitting hat is constructed and the sampling algorithm becomes fast; on
the other hand large tables are needed and the setup is very slow. For moderate
values of sqhratio (e.g. `0.9' or `0.8') the sampling is slower but the
required tables are smaller and the setup is not so slow.

   It follows from the above explanations that TABL is always requiring a slow
setup and that it is not very well suited for heavy-tailed distributions.

How To Use
..........

For using the TABL method UNU.RAN needs a bounded interval to which the
generated variates can be restricted and information about all local extrema of
the distribution. For unimodal densities it is sufficient to provide the mode
of the distribution. For the case of a built-in unimodal distribution with
bounded domain all these information is present in the distribution object and
thus no extra input is necessary (see example_TABL1 below).

   For a built-in unimodal distribution with unbounded domain we should specify
the cut-off values for the tails. This can be done with the
`unur_tabl_set_boundary' call (see example_TABL2 below). For the case that we
do not set these boundaries the default values of `+/- 1.e20' are used. We can
see in example_TABL1 that this still works fine for many standard distributions.

   For the case of a multimodal distribution we have to set the regions of
monotonicity (called slopes) explicitly using the `unur_tabl_set_slopes'
command (see example_TABL3 below).

   To controll the fit of the hat and the size of the tables and thus the speed
of the setup and the sampling it is most convenient to use the
`unur_tabl_set_max_sqhratio' call. The default is `0.9' which is a sensible
value for most distributions and applications. If very large samples of a
distribution are required or the evaluation of a density is very slow it may be
useful to increase the sqhratio to eg. `0.95' or even `0.99'. With the
`unur_tabl_get_sqhratio' call we can check which sqhratio was really reached.
If that value is below the desired value it is necessary to increase the
maximal number of subintervals, which defaults to `1000', using the
`unur_tabl_set_max_intervals' call.  The `unur_tabl_get_n_intervals' call can
be used to find out the number of subintervals the setup calculated.

   It is also possible to set the number of intervals and their respective
boundaries by means of the `unur_tabl_set_cpoints' call.

   It is also possible to use method TABL for correlation induction (variance
reduction) by setting of an auxiliary uniform random number generator via the
`unur_set_urng_aux' call. (Notice that this must be done after a possible
`unur_set_urng' call.)  However, this only works when immediate acceptance is
switched of by a `unur_tabl_set_variant_ia' call.

Function reference
------------------

 -- Function: UNUR_PAR* unur_tabl_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_tabl_set_variant_ia (UNUR_PAR* PARAMETERS, int USE_IA)
     Use immediate acceptance when USE_IA is set to `TRUE'.  This technique
     requires less uniform. If it is set to `FALSE', "classical"
     acceptance/rejection from hat distribution is used.

     _Notice:_ Auxiliary uniform random number generators for correlation
     induction (variance reduction) can only be used when "classical"
     acceptance/rejection is used.

     Default: `TRUE'.

 -- Function: int unur_tabl_set_cpoints (UNUR_PAR* PARAMETERS, int N_CPOINTS,
          const DOUBLE* CPOINTS)
     Set construction points for the hat function. If STP is `NULL' than a
     heuristic rule of thumb is used to get N_STP construction points. This is
     the default behavior.

     The default number of construction points is `30'.

 -- Function: int unur_tabl_set_nstp (UNUR_PAR* PARAMETERS, int N_STP)
     Set number of construction points for the hat function. N_STP must be
     greater than zero. After the setup there are about N_STP construction
     points. However it might be larger when a small fraction is given by the
     `unur_tabl_set_areafraction' call.  It also might be smaller for some
     variants.

     Default is `30'.

 -- Function: int unur_tabl_set_useear (UNUR_PAR* PARAMETERS, int USEEAR)
     If USEEAR is set to `TRUE', the "equal area rule" is used, the given
     slopes are partitioned in such a way that the area below the hat function
     in each subinterval ("stripe") has the same area (except the last the last
     interval which can be smaller).  The area can be set by means of the
     `unur_tabl_set_areafraction' call.

     Default is `TRUE'.

 -- Function: int unur_tabl_set_areafraction (UNUR_PAR* PARAMETERS, double
          FRACTION)
     Set parameter for the equal area rule. During the setup a piecewise
     constant hat is constructed, such that the area below each of these pieces
     (strips) is the same and equal to the (given) area below the PDF times
     FRACTION (which must be greater than zero).

     _Important:_ If the area below the PDF is not set in the distribution
     object, then 1 is assumed.

     Default is `0.1'.

 -- Function: int unur_tabl_set_usedars (UNUR_PAR* PARAMETERS, int USEDARS)
     If USEDARS is set to `TRUE', "derandomized adaptive rejection sampling"
     (DARS) is used in the setup.  Intervals, where the area between hat and
     squeeze is too large compared to the average area between hat and squeeze
     over all intervals, are split.  This procedure is repeated until the ratio
     between squeeze and hat exceeds the bound given by
     `unur_tabl_set_max_sqhratio' call or the maximum number of intervals is
     reached. Moreover, it also aborts when no more intervals can be found for
     splitting.

     For finding splitting points the arc-mean rule (a mixture of arithmetic
     mean and harmonic mean) is used.

     Default is `TRUE'.

 -- Function: int unur_tabl_set_darsfactor (UNUR_PAR* PARAMETERS, double FACTOR)
     Set factor for "derandomized adaptive rejection sampling".  This factor is
     used to determine the segments that are "too large", that is, all segments
     where the area between squeeze and hat is larger than FACTOR times the
     average area over all intervals between squeeze and hat.  Notice that all
     segments are split when FACTOR is set to `0.', and that there is no
     splitting at all when FACTOR is set to `UNUR_INFINITY'.

     Default is `0.99'. There is no need to change this parameter.

 -- Function: int unur_tabl_set_variant_splitmode (UNUR_PAR* PARAMETERS,
          unsigned SPLITMODE)
     There are three variants for adaptive rejection sampling. These differ in
     the way how an interval is split:
    splitmode `1'
          use the generated point to split the interval.

    splitmode `2'
          use the mean point of the interval.

    splitmode `3'
          use the arcmean point; suggested for distributions with heavy tails.

     Default is splitmode `2'.

 -- Function: int unur_tabl_set_max_sqhratio (UNUR_PAR* PARAMETERS, double
          MAX_RATIO)
     Set upper bound for the ratio (area below squeeze) / (area below hat).  It
     must be a number between 0 and 1.  When the ratio exceeds the given number
     no further construction points are inserted via DARS in the setup.

     For the case of ARS (unur_tabl_set_usedars() must be set to `FALSE'): Use
     `0' if no construction points should be added after the setup.  Use `1' if
     added new construction points should not be stopped until the maximum
     number of construction points is reached.  If MAX_RATIO is close to one,
     many construction points are used.

     Default is `0.9'.

 -- Function: double unur_tabl_get_sqhratio (const UNUR_GEN* GENERATOR)
     Get the current ratio (area below squeeze) / (area below hat) for the
     generator.  (In case of an error `UNUR_INFINITY' is returned.)

 -- Function: double unur_tabl_get_hatarea (const UNUR_GEN* GENERATOR)
     Get the area below the hat for the generator.  (In case of an error
     `UNUR_INFINITY' is returned.)

 -- Function: double unur_tabl_get_squeezearea (const UNUR_GEN* GENERATOR)
     Get the area below the squeeze for the generator.  (In case of an error
     `UNUR_INFINITY' is returned.)

 -- Function: int unur_tabl_set_max_intervals (UNUR_PAR* PARAMETERS, int
          MAX_IVS)
     Set maximum number of intervals.  No construction points are added in or
     after the setup when the number of intervals suceeds MAX_IVS.

     Default is `1000'.

 -- Function: int unur_tabl_get_n_intervals (const UNUR_GEN* GENERATOR)
     Get the current number of intervals.  (In case of an error 0 is returned.)

 -- Function: int unur_tabl_set_slopes (UNUR_PAR* PARAMETERS, const DOUBLE*
          SLOPES, int N_SLOPES)
     Set slopes for the PDF.  A slope <a,b> is an interval [a,b] or [b,a] where
     the PDF is monotone and PDF(a) >= PDF(b).  The list of slopes is given by
     an array SLOPES where each consecutive tuple (i.e. `(slopes[0],
     slopes[1])', `(slopes[2], slopes[3])', etc.) defines one slope.  Slopes
     must be sorted (i.e. both `slopes[0]' and `slopes[1]' must not be greater
     than any entry of the slope `(slopes[2], slopes[3])', etc.)  and must not
     be overlapping. Otherwise no slopes are set and UNUR_ERRNO is set to
     `UNUR_ERR_PAR_SET'.

     _Notice:_ N_SLOPES is the number of slopes (and not the length of the
     array SLOPES).

     _Notice_ that setting slopes resets the given domain for the distribution.
     However, in case of a standard distribution the area below the PDF is not
     updated.

 -- Function: int unur_tabl_set_guidefactor (UNUR_PAR* PARAMETERS, double
          FACTOR)
     Set factor for relative size of the guide table for indexed search (see
     also method DGT *note DGT::). It must be greater than or equal to `0'.
     When set to `0', then sequential search is used.

     Default is `1'.

 -- Function: int unur_tabl_set_boundary (UNUR_PAR* PARAMETERS, double LEFT,
          double RIGHT)
     Set the left and right boundary of the computation interval.  The
     piecewise hat is only constructed inside this interval. The probability
     outside of this region must not be of computational relevance.  Of course
     `+/- UNUR_INFINITY' is not allowed.

     Default is `-1.e20,1.e20'.

 -- Function: int unur_tabl_chg_truncated (UNUR_GEN* GEN, double LEFT, double
          RIGHT)
     Change the borders of the domain of the (truncated) distribution.

     Notice that the given truncated domain must be a subset of the domain of
     the given distribution. The generator always uses the intersection of the
     domain of the distribution and the truncated domain given by this call.
     The hat function will not be changed.

     _Important:_ The ratio between the area below the hat and the area below
     the squeeze changes when the sampling region is restricted. In particalur
     it becomes (very) large when sampling from the (far) tail of the
     distribution. Then it is better to create a generator object for the tail
     of distribution only.

     _Important:_ This call does not work for variant `IA' (immediate
     acceptance). In this case UNU.RAN switches _automatically_ to variant `RH'
     (use "classical" acceptance/rejection from hat distribution) and does
     revert to the variant originally set by the user.

     _Important:_ It is not a good idea to use adaptave rejection sampling while
     sampling from a domain that is a strict subset of the domain that has been
     used to construct the hat.  For that reason adaptive adding of
     construction points is _automatically disabled_ by this call.

     _Important:_ If the CDF of the hat is (almost) the same for LEFT and RIGHT
     and (almost) equal to `0' or `1', then the truncated domain is not changed
     and the call returns an error code.

 -- Function: int unur_tabl_set_verify (UNUR_PAR* PARAMETERS, int VERIFY)
 -- Function: int unur_tabl_chg_verify (UNUR_GEN* GENERATOR, int VERIFY)
     Turn verifying of algorithm while sampling on/off.  If the condition
     squeeze(x) <= PDF(x) <= hat(x) is violated for some x then `unur_errno' is
     set to `UNUR_ERR_GEN_CONDITION'. However notice that this might happen due
     to round-off errors for a few values of x (less than 1%).

     Default is `FALSE'.

 -- Function: int unur_tabl_set_pedantic (UNUR_PAR* PARAMETERS, int PEDANTIC)
     Sometimes it might happen that `unur_init' has been executed successfully.
     But when additional construction points are added by adaptive rejection
     sampling, the algorithm detects that the PDF is not monotone in the given
     slopes.

     With PEDANTIC being `TRUE', the sampling routine is exchanged by a routine
     that simply returns `UNUR_INFINITY' indicating an error.

     Default is `FALSE'.

5.3.16 TDR  -  Transformed Density Rejection
--------------------------------------------

Required:
     T-concave PDF, dPDF

Optional:
     mode

Speed:
     Set-up: slow, Sampling: fast

Reinit:
     supported

Reference:
     [GWa92] [HWa95] [HLD04: Cha.4]



   TDR is an acceptance/rejection method that uses the concavity of a
transformed density to construct hat function and squeezes automatically. Such
PDFs are called T-concave. Currently the following transformations are
implemented and can be selected by setting their `c'-values by a
`unur_tdr_set_c' call:

`c = 0'
     T(x) = log(x)

`c = -0.5'
     T(x) = -1/sqrt(x)      (Default)

   In future releases the transformations T(x) = -(x)^c will be available for
any c with 0 > c > -1.  Notice that if a PDF is T-concave for a c then it also
T-concave for every c'<c. However the performance decreases when c' is smaller
than c. For computational reasons we suggest the usage of c = -0.5 (this is the
default).  For c <= -1 the hat is not bounded any more if the domain of the PDF
is unbounded. But in the case of a bounded domain using method TABL is
preferred to a TDR with c < -1 (except in a few special cases).

   We offer three variants of the algorithm.

`GW'
     squeezes between construction points

`PS'
     squeezes proportional to hat function       (Default)

`IA'
     same as variant PS but uses a compositon method with "immediate
     acceptance" in the region below the squeeze.

   `GW' has a slightly faster setup but higher marginal generation times.  `PS'
is faster than `GW'. `IA' uses less uniform random numbers and is therefore
faster than `PS'.

   It is also possible to evaluate the inverse of the CDF of the hat
distribution directly using the `unur_tdr_eval_invcdfhat' call.

   There are lots of parameters for these methods, see below.

   It is possible to use this method for correlation induction by setting an
auxiliary uniform random number generator via the `unur_set_urng_aux' call.
(Notice that this must be done after a possible `unur_set_urng' call.)  When an
auxiliary generator is used then the number of uniform random numbers from the
first URNG that are used for one generated random variate is constant and given
in the following table:

`GW ... 2'

`PS ... 2'

`IA ... 1'

   There exists a test mode that verifies whether the conditions for the method
are satisfied or not. It can be switched on by calling `unur_tdr_set_verify' and
`unur_tdr_chg_verify', respectively.  Notice however that sampling is (much)
slower then.

   For densities with modes not close to 0 it is suggested to set either the
mode or the center of the distribution by the `unur_distr_cont_set_mode' or
`unur_distr_cont_set_center' call.  The latter is the approximate location of
the mode or the mean of the distribution. This location provides some
information about the main part of the PDF and is used to avoid numerical
problems.

   It is possible to use this method for generating from truncated
distributions. It even can be changed for an existing generator object by an
`unur_tdr_chg_truncated' call.

   It is possible to change the parameters and the domain of the chosen
distribution and run `unur_reinit' to reinitialize the generator object.

   _Important:_ The ratio between the area below the hat and the area below the
squeeze changes when the sampling region is restricted. Especially it becomes
(very) small when sampling from the (far) tail of the distribution. Then it is
better to create a new generator object for the tail of the distribution only.

Function reference
------------------

 -- Function: UNUR_PAR* unur_tdr_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_tdr_set_c (UNUR_PAR* PARAMETERS, double C)
     Set parameter C for transformation T.  Currently only values between 0 and
     -0.5 are allowed.  If `c' is between 0 and -0.5 it is set to -0.5.

     Default is `-0.5'.

 -- Function: int unur_tdr_set_variant_gw (UNUR_PAR* PARAMETERS)
     Use original version with squeezes between construction points as proposed
     by Gilks & Wild  (1992).

 -- Function: int unur_tdr_set_variant_ps (UNUR_PAR* PARAMETERS)
     Use squeezes proportional to the hat function. This is faster than the
     original version.  This is the default.

 -- Function: int unur_tdr_set_variant_ia (UNUR_PAR* PARAMETERS)
     Use squeezes proportional to the hat function together with a composition
     method that required less uniform random numbers.

 -- Function: int unur_tdr_set_usedars (UNUR_PAR* PARAMETERS, int USEDARS)
     If USEDARS is set to `TRUE', "derandomized adaptive rejection sampling"
     (DARS) is used in setup.  Intervals where the area between hat and squeeze
     is too large compared to the average area between hat and squeeze over all
     intervals are split.  This procedure is repeated until the ratio between
     area below squeeze and area below hat exceeds the bound given by
     `unur_tdr_set_max_sqhratio' call or the maximum number of intervals is
     reached. Moreover, it also aborts when no more intervals can be found for
     splitting.

     For finding splitting points the following rules are used (in this order,
     i.e., is if the first rule cannot be applied, the next one is used):
       1. Use the expected value of adaptive rejection sampling.

       2. Use the arc-mean rule (a mixture of arithmetic mean and harmonic
          mean).

       3. Use the arithmetic mean of the interval boundaries.
          Notice, however, that for unbounded intervals neither rule 1 nor rule
     3 can be used.

     As an additional feature, it is possible to choose amoung these rules.  If
     USEDARS is set to `1' or `TRUE' the expected point (rule 1) is used (it
     switches to rule 2 for a particular interval if rule 1 cannot be applied).
     If it is set to `2' the arc-mean rule is used.  If it is set to `3' the
     mean is used.  Notice that rule 3 can only be used if the domain of the
     distribution is bounded. It is faster than the other two methods but for
     heavy-tailed distribution and large domain the hat converges extremely
     slowly.

     The default depends on the given construction points.  If the user has
     provided such points via a `unur_tdr_set_cpoints' call, then USEDARS is
     set to `FALSE' by default, i.e., there is no further splitting.  If the
     user has only given the number of construction points (or only uses the
     default number), then USEDARS is set to `TRUE' (i.e., use rule 1).

 -- Function: int unur_tdr_set_darsfactor (UNUR_PAR* PARAMETERS, double FACTOR)
     Set factor for "derandomized adaptive rejection sampling".  This factor is
     used to determine the intervals that are "too large", that is, all
     intervals where the area between squeeze and hat is larger than FACTOR
     times the average area over all intervals between squeeze and hat.  Notice
     that all intervals are split when FACTOR is set to `0.', and that there is
     no splitting at all when FACTOR is set to `UNUR_INFINITY'.

     Default is `0.99'. There is no need to change this parameter.

 -- Function: int unur_tdr_set_cpoints (UNUR_PAR* PARAMETERS, int N_STP, const
          DOUBLE* STP)
     Set construction points for the hat function. If STP is `NULL' than a
     heuristic rule of thumb is used to get N_STP construction points. This is
     the default behavior.

     The default number of construction points is 30.

 -- Function: int unur_tdr_set_reinit_percentiles (UNUR_PAR* PARAMETERS, int
          N_PERCENTILES, const DOUBLE* PERCENTILES)
 -- Function: int unur_tdr_chg_reinit_percentiles (UNUR_GEN* GENERATOR, int
          N_PERCENTILES, const DOUBLE* PERCENTILES)
     By default, when the GENERATOR object is reinitialized, it used the same
     construction points as for the initialization procedure.  Often the
     underlying distribution object has been changed only moderately. For
     example, the full conditional distribution of a multivariate distribution.
     In this case it might be more appropriate to use percentilesm of the hat
     function for the last (unchanged) distribution. PERCENTILES must then be a
     pointer to an ordered array of numbers between `0.01' and `0.99'.  If
     PERCENTILES is `NULL', then a heuristic rule of thumb is used to get
     N_PERCENTILES values for these percentiles.  Notice that N_PERCENTILES
     must be at least `2', otherwise defaults are used.  (Then the first and
     third quartiles are used by default.)

 -- Function: int unur_tdr_set_reinit_ncpoints (UNUR_PAR* PARAMETERS, int
          NCPOINTS)
 -- Function: int unur_tdr_chg_reinit_ncpoints (UNUR_GEN* GENERATOR, int
          NCPOINTS)
     When reinit fails with the given construction points or the percentiles of
     the old hat function, another trial is undertaken with NCPOINTS
     construction points. NCPOINTS must be at least `10'.

     Default: `50'

 -- Function: int unur_tdr_chg_truncated (UNUR_GEN* GEN, double LEFT, double
          RIGHT)
     Change the borders of the domain of the (truncated) distribution.

     Notice that the given truncated domain must be a subset of the domain of
     the given distribution. The generator always uses the intersection of the
     domain of the distribution and the truncated domain given by this call.
     The hat function will not be changed and there is no need to run
     `unur_reinit'.  _Important:_ The ratio between the area below the hat and
     the area below the squeeze changes when the sampling region is restricted.
     In particular it becomes (very) large when sampling from the (far) tail of
     the distribution. Then it is better to create a generator object for the
     tail of distribution only.

     _Important:_ This call does not work for variant `IA' (immediate
     acceptance). In this case UNU.RAN switches _automatically_ to variant `PS'.

     _Important:_ It is not a good idea to use adaptave rejection sampling while
     sampling from a domain that is a strict subset of the domain that has been
     used to construct the hat.  For that reason adaptive adding of
     construction points is _automatically disabled_ by this call.

     _Important:_ If the CDF of the hat is (almost) the same for LEFT and RIGHT
     and (almost) equal to `0' or `1', then the truncated domain is not changed
     and the call returns an error code.

 -- Function: int unur_tdr_set_max_sqhratio (UNUR_PAR* PARAMETERS, double
          MAX_RATIO)
     Set upper bound for the ratio (area below squeeze) / (area below hat).  It
     must be a number between 0 and 1.  When the ratio exceeds the given number
     no further construction points are inserted via adaptive rejection
     sampling.  Use 0 if no construction points should be added after the setup.
     Use 1 if added new construction points should not be stopped until the
     maximum number of construction points is reached.

     Default is `0.99'.

 -- Function: double unur_tdr_get_sqhratio (const UNUR_GEN* GENERATOR)
     Get the current ratio (area below squeeze) / (area below hat) for the
     generator.  (In case of an error `UNUR_INFINITY' is returned.)

 -- Function: double unur_tdr_get_hatarea (const UNUR_GEN* GENERATOR)
     Get the area below the hat for the generator.  (In case of an error
     `UNUR_INFINITY' is returned.)

 -- Function: double unur_tdr_get_squeezearea (const UNUR_GEN* GENERATOR)
     Get the area below the squeeze for the generator.  (In case of an error
     `UNUR_INFINITY' is returned.)

 -- Function: int unur_tdr_set_max_intervals (UNUR_PAR* PARAMETERS, int MAX_IVS)
     Set maximum number of intervals.  No construction points are added after
     the setup when the number of intervals suceeds MAX_IVS.  It is increased
     automatically to twice the number of construction points if this is larger.

     Default is `100'.

 -- Function: int unur_tdr_set_usecenter (UNUR_PAR* PARAMETERS, int USECENTER)
     Use the center as construction point. Default is `TRUE'.

 -- Function: int unur_tdr_set_usemode (UNUR_PAR* PARAMETERS, int USEMODE)
     Use the (exact!) mode as construction point.  Notice that the behavior of
     the algorithm is different to simply adding the mode in the list of
     construction points via a `unur_tdr_set_cpoints' call. In the latter case
     the mode is treated just like any other point. However, when `usemode' is
     `TRUE', the tangent in the mode is always set to 0. Then the hat of the
     transformed density can never cut the x-axis which must never happen if c
     < 0, since otherwise the hat would not be bounded.

     Default is `TRUE'.

 -- Function: int unur_tdr_set_guidefactor (UNUR_PAR* PARAMETERS, double FACTOR)
     Set factor for relative size of the guide table for indexed search (see
     also method DGT *note DGT::). It must be greater than or equal to `0'.
     When set to `0', then sequential search is used.

     Default is 2.

 -- Function: int unur_tdr_set_verify (UNUR_PAR* PARAMETERS, int VERIFY)
 -- Function: int unur_tdr_chg_verify (UNUR_GEN* GENERATOR, int VERIFY)
     Turn verifying of algorithm while sampling on/off.  If the condition
     squeeze(x) <= PDF(x) <= hat(x) is violated for some x then `unur_errno' is
     set to `UNUR_ERR_GEN_CONDITION'. However notice that this might happen due
     to round-off errors for a few values of x (less than 1%).

     Default is `FALSE'.

 -- Function: int unur_tdr_set_pedantic (UNUR_PAR* PARAMETERS, int PEDANTIC)
     Sometimes it might happen that `unur_init' has been executed successfully.
     But when additional construction points are added by adaptive rejection
     sampling, the algorithm detects that the PDF is not T-concave.

     With PEDANTIC being `TRUE', the sampling routine is exchanged by a routine
     that simply returns `UNUR_INFINITY'. Otherwise the new point is not added
     to the list of construction points. At least the hat function remains
     T-concave.

     Setting PEDANTIC to `FALSE' allows sampling from a distribution which is
     "almost" T-concave and small errors are tolerated. However it might happen
     that the hat function cannot be improved significantly. When the hat
     functions that has been constructed by the `unur_init' call is extremely
     large then it might happen that the generation times are extremely high
     (even hours are possible in extremely rare cases).

     Default is `FALSE'.

 -- Function: double unur_tdr_eval_invcdfhat (const UNUR_GEN* GENERATOR, double
          U, double* HX, double* FX, double* SQX)
     Evaluate the inverse of the CDF of the hat distribution at U.  As a side
     effect the values of the hat, the density, and the squeeze at the computed
     point x are stored in HX, FX, and SQX, respectively. However, these
     computations are suppressed if the corresponding variable is set to `NULL'.

     If U is out of the domain [0,1] then `unur_errno' is set to
     `UNUR_ERR_DOMAIN' and the respective bound of the domain of the
     distribution are returned (which is `-UNUR_INFINITY' or `UNUR_INFINITY' in
     the case of unbounded domains).

     _Important:_ This call does not work for variant `IA' (immediate
     acceptance). In this case the hat CDF is evaluated as if variant `PS' is
     used.

     _Notice_: This function always evaluates the inverse CDF of the hat
     distribution. A call to `unur_tdr_chg_truncated' call has no effect.

5.3.17 UTDR  -  Universal Transformed Density Rejection
-------------------------------------------------------

Required:
     T-concave PDF, mode, approximate area

Speed:
     Set-up: moderate, Sampling: Moderate

Reinit:
     supported

Reference:
     [HWa95] [HLD04: Sect.4.5.4; Alg.4.4]



   UTDR is based on the transformed density rejection and uses three almost
optimal points for constructing hat and squeezes.  It works for all T-concave
distributions with T(x) = -1/sqrt(x).

   It requires the PDF and the (exact) location of the mode.  Notice that if no
mode is given at all, a (slow) numerical mode finder will be used.  Moreover
the approximate area below the given PDF is used.  (If no area is given for the
distribution the algorithm assumes that it is approximately 1.)  The rejection
constant is bounded from above by 4 for all T-concave distributions.

How To Use
..........

UTDR works for any continuous univariate distribution object with given
T-concave PDF (with T(x) = -1/sqrt(x),) mode and approximate area below PDF.

   When the PDF does not change at the mode for varying parameters, then this
value can be set with `unur_utdr_set_pdfatmode' to avoid some computations.
Since this value will not be updated any more when the parameters of the
distribution are changed, the `unur_utdr_chg_pdfatmode' call is necessary to do
this manually.

   It is possible to change the parameters and the domain of the chosen
distribution and run `unur_reinit' to reinitialize the generator object.
Notice, that derived parameters like the mode must also be (re-) set if the
parameters or the domain has be changed.  Moreover, if the PDF at the mode has
been provided by a `unur_utdr_set_pdfatmode' call, additionally
`unur_utdr_chg_pdfatmode' must be used (otherwise this call is not necessary
since then this figure is computed directly from the PDF).

   There exists a test mode that verifies whether the conditions for the method
are satisfied or not. It can be switched on by calling `unur_utdr_set_verify'
and `unur_utdr_chg_verify', respectively.  Notice however that sampling is
slower then.

Function reference
------------------

 -- Function: UNUR_PAR* unur_utdr_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_utdr_set_pdfatmode (UNUR_PAR* PARAMETERS, double FMODE)
     Set pdf at mode.  When set, the PDF at the mode is never changed.  This is
     to avoid additional computations, when the PDF does not change when
     parameters of the distributions vary.  It is only useful when the PDF at
     the mode does not change with changing parameters for the distribution.

     Default: not set.

 -- Function: int unur_utdr_set_cpfactor (UNUR_PAR* PARAMETERS, double
          CP_FACTOR)
     Set factor for position of left and right construction point.  The
     CP_FACTOR is used to find almost optimal construction points for the hat
     function.  There is no need to change this factor in almost all situations.

     Default is `0.664'.

 -- Function: int unur_utdr_set_deltafactor (UNUR_PAR* PARAMETERS, double DELTA)
     Set factor for replacing tangents by secants.  higher factors increase the
     rejection constant but reduces the risk of serious round-off errors.
     There is no need to change this factor it almost all situations.

     Default is `1.e-5'.

 -- Function: int unur_utdr_set_verify (UNUR_PAR* PARAMETERS, int VERIFY)
 -- Function: int unur_utdr_chg_verify (UNUR_GEN* GENERATOR, int VERIFY)
     Turn verifying of algorithm while sampling on/off.  If the condition
     squeeze(x) <= PDF(x) <= hat(x) is violated for some x then `unur_errno' is
     set to `UNUR_ERR_GEN_CONDITION'. However notice that this might happen due
     to round-off errors for a few values of x (less than 1%).

     Default is `FALSE'.

 -- Function: int unur_utdr_chg_pdfatmode (UNUR_GEN* GENERATOR, double FMODE)
     Change PDF at mode of distribution.  `unur_reinit' must be executed before
     sampling from the generator again.

5.4 Methods for continuous empirical univariate distributions
=============================================================

Overview of methods
-------------------

Methods for continuous empirical univariate distributions
sample with `unur_sample_cont'

EMPK:  Requires an observed sample.
EMPL:  Requires an observed sample.

Example
-------

     /* ------------------------------------------------------------- */
     /* File: example_emp.c                                          */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */

     /* Example how to sample from an empirial continuous univariate  */
     /* distribution.                                                 */

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;
       double x;

       /* data points                                                 */
       double data[15] = { -0.1,  0.05, -0.5,   0.08,  0.13,\
     		      -0.21,-0.44, -0.43, -0.33, -0.3, \
     		       0.18, 0.2,  -0.37, -0.29, -0.9 };

       /* Declare the three UNURAN objects.                           */
       UNUR_DISTR *distr;    /* distribution object                   */
       UNUR_PAR   *par;      /* parameter object                      */
       UNUR_GEN   *gen;      /* generator object                      */

       /* Create a distribution object and set empirical sample.      */
       distr = unur_distr_cemp_new();
       unur_distr_cemp_set_data(distr, data, 15);

       /* Choose a method: EMPK.                                      */
       par = unur_empk_new(distr);

       /* Set smooting factor.                                        */
       unur_empk_set_smoothing(par, 0.8);

       /* Create the generator object.                                */
       gen = unur_init(par);

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* It is possible to reuse the distribution object to create   */
       /* another generator object. If you do not need it any more,   */
       /* it should be destroyed to free memory.                      */
       unur_distr_free(distr);

       /* Now you can use the generator object `gen' to sample from   */
       /* the distribution. Eg.:                                      */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* When you do not need the generator object any more, you     */
       /* can destroy it.                                             */
       unur_free(gen);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

Example (String API)
--------------------

     /* ------------------------------------------------------------- */
     /* File: example_emp_str.c                                       */
     /* ------------------------------------------------------------- */
     /* String API.                                                   */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */

     /* Example how to sample from an empirial continuous univariate  */
     /* distribution.                                                 */

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;
       double x;

       /* Declare UNURAN generator object.                            */
       UNUR_GEN   *gen;      /* generator object                      */

       /* Create the generator object.                                */
       gen = unur_str2gen("distr = cemp; \
                              data=(-0.10, 0.05,-0.50, 0.08, 0.13,   \
                                    -0.21,-0.44,-0.43,-0.33,-0.30,   \
                                     0.18, 0.20,-0.37,-0.29,-0.90) & \
                           method=empk; smoothing=0.8");

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* Now you can use the generator object `gen' to sample from   */
       /* the distribution. Eg.:                                      */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* When you do not need the generator object any more, you     */
       /* can destroy it.                                             */
       unur_free(gen);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

5.4.1 EMPK  -  EMPirical distribution with Kernel smoothing
-----------------------------------------------------------

Required:
     observed sample

Speed:
     Set-up: slow (as sample is sorted), Sampling: fast (depends on kernel)

Reinit:
     not implemented

Reference:
     [HLa00] [HLD04: Sect.12.1.2]



   EMPK generates random variates from an empirical distribution that is given
by an observed sample. The idea is that simply choosing a random point from the
sample and to return it with some added noise results in a method that has very
nice properties, as it can be seen as sampling from a kernel density estimate.
If the underlying distribution is continuous, especially the fine structur of
the resulting empirical distribution is much better than using only resampling
without noise.

   Clearly we have to decide about the density of the noise (called kernel) and
about the standard deviation of the noise.  The mathematical theory of kernel
density estimation shows us that we are comparatively free in choosing the
kernel. It also supplies us with a simple formula to compute the optimal
standarddeviation of the noise, called bandwidth (or window width) of the
kernel.

   The variance of the estimated density is slightly larger than that of the
observed sample. However, this can be easily corrected if required.

   There is also a correction (mirroring technique) for distributions with
non-negative support.

   A simple robust reference method is implemented to find a good standard
deviation of the noise (i.e. the bandwidth of kernel density estimation). For
some cases (e.g. densities with two or more sharp distinct peaks) there kernel
density estimation can be adjusted by changing the smoothness factor and the so
called beta factor.

How To Use
..........

EMPK uses empirical distributions. The main parameter is the choice if of
kernel density. The most important kernels can be set by `unur_empk_set_kernel'.
Additionally generators for other kernels can be used by using
`unur_empk_set_kernelgen' instead.  Additionally variance correction and a
correction for non-negative variates can be switched on.

   The two other parameters (smoothing factor and beta factor) are only useful
for people knowing the theory of kernel density estimation. It is not necessary
to change them if the true underlying distribution is somehow comparable with a
bell-shaped curve, even skewed or with some not too sharp extra peaks.  In all
these cases the simple robust reference method implemented to find a good
standard deviation of the noise (i.e. the bandwidth of kernel density
estimation) should give sensible results.  However, it might be necessary to
overwrite this automatic method to find the bandwidth eg. when resampling from
data with two or more sharp distinct peaks. Then the distribution has nearly
discrete components as well and our automatic method may easily choose too
large a bandwidth which results in an empirical distribution which is
oversmoothed (i.e. it has lower peaks than the original distribution). Then it
is recommended to decrease the bandwidth using the `unur_empk_set_smoothing'
call. A smoothing factor of `1' is the default. A smoothing factor of `0' leads
to naive resampling of the data. Thus an appropriate value between these
extremes should be choosen. We recommend to consult a reference on kernel
smoothing when doing so; but it is not a simple problem to determine an optimal
bandwidth for distributions with sharp peaks.

   In general, for most applications it is perfectly ok to use the default
values offered. Unless you have some knowledge on density estimation we do not
recommend to change anything.  There are two exceptions:

  A. In the case that the unknown underlying distribution is not continuous but
     discrete you should "turn off" the adding of the noise by setting:
          unur_empk_set_smoothing(par, 0.)

  B. In the case that you are especially interested in a fast sampling
     algorithm use the call
          unur_empk_set_kernel(par, UNUR_DISTR_BOXCAR);
     to change the used noise distribution from the default Gaussian
     distribution to the uniform distribution.

Function reference
------------------

 -- Function: UNUR_PAR* unur_empk_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_empk_set_kernel (UNUR_PAR* PARAMETERS, unsigned KERNEL)
     Select one of the supported kernel distributions. Currently the following
     kernels are supported:

    `UNUR_DISTR_GAUSSIAN'
          Gaussian (normal) kernel

    `UNUR_DISTR_EPANECHNIKOV'
          Epanechnikov kernel

    `UNUR_DISTR_BOXCAR'
          Boxcar (uniform, rectangular) kernel

    `UNUR_DISTR_STUDENT'
          t3 kernel (Student's distribution with 3 degrees of freedom)

    `UNUR_DISTR_LOGISTIC'
          logistic kernel

     For other kernels (including kernels with Student's distribution with
     other than 3 degrees of freedom) use the `unur_empk_set_kernelgen' call.

     It is not possible to call `unur_empk_set_kernel' twice.

     Default is the Gaussian kernel.

 -- Function: int unur_empk_set_kernelgen (UNUR_PAR* PARAMETERS, const
          UNUR_GEN* KERNELGEN, double ALPHA, double KERNELVAR)
     Set generator for the kernel used for density estimation.

     ALPHA is used to compute the optimal bandwidth from the point of view of
     minimizing the mean integrated square error (MISE).  It depends on the
     kernel K and is given by
          alpha(K) = Var(K)^(-2/5){ \int K(t)^2 dt}^(1/5)
     For standard kernels (see above) alpha is computed by the algorithm.

     KERNVAR is the variance of the used kernel. It is only required for the
     variance corrected version of density estimation (which is used by
     default); otherwise it is ignored.  If KERNELVAR is nonpositive, variance
     correction is disabled.  For standard kernels (see above) KERNVAR is
     computed by the algorithm.

     It is not possible to call `unur_empk_set_kernelgen' after a standard
     kernel has been selected by a `unur_empk_set_kernel' call.

     Notice that the uniform random number generator of the kernel generator is
     overwritten during the `unur_init' call and at each `unur_chg_urng' call
     with the uniform generator used for the empirical distribution.

     Default is the Gaussian kernel.

 -- Function: int unur_empk_set_beta (UNUR_PAR* PARAMETERS, double BETA)
     BETA is used to compute the optimal bandwidth from the point of view of
     minimizing the mean integrated square error (MISE).  BETA depends on the
     (unknown) distribution of the sampled data points.  By default Gaussian
     distribution is assumed for the sample (BETA = 1.3637439). There is no
     requirement to change BETA.

     Default: `1.3637439'

 -- Function: int unur_empk_set_smoothing (UNUR_PAR* PARAMETERS, double
          SMOOTHING)
 -- Function: int unur_empk_chg_smoothing (UNUR_GEN* GENERATOR, double
          SMOOTHING)
     Set and change the smoothing factor.  The smoothing factor controlles how
     "smooth" the resulting density estimation will be. A smoothing factor
     equal to `0' results in naive resampling. A very large smoothing factor
     (together with the variance correction) results in a density which is
     approximately equal to the kernel.  Default is 1 which results in a
     smoothing parameter minimising the MISE (mean integrated squared error) if
     the data are not too far away from normal. If a large smoothing factor is
     used, then variance correction must be switched on.

     Default: `1'

 -- Function: int unur_empk_set_varcor (UNUR_PAR* PARAMETERS, int VARCOR)
 -- Function: int unur_empk_chg_varcor (UNUR_GEN* GENERATOR, int VARCOR)
     Switch variance correction in generator on/off.  If VARCOR is `TRUE' then
     the variance of the used density estimation is the same as the sample
     variance. However this increases the MISE of the estimation a little bit.

     Default is `FALSE'.

 -- Function: int unur_empk_set_positive (UNUR_PAR* PARAMETERS, int POSITIVE)
     If POSITIVE is `TRUE' then only nonnegative random variates are generated.
     This is done by means of a mirroring technique.

     Default is `FALSE'.

5.4.2 EMPL  -  EMPirical distribution with Linear interpolation
---------------------------------------------------------------

Required:
     observed sample

Speed:
     Set-up: slow (as sample is sorted), Sampling: very fast (inversion)

Reinit:
     not implemented

Reference:
     [HLa00] [HLD04: Sect.12.1.3]



   EMPL generates random variates from an empirical distribution that is given
by an observed sample. This is done by linear interpolation of the empirical
CDF. Although this method is suggested in the books of Law and Kelton (2000) and
Bratly, Fox, and Schrage (1987) we do not recommend this method at all since it
has many theoretical drawbacks: The variance of empirical distribution function
does not coincide with the variance of the given sample. Moreover, when the
sample increases the empirical density function does not converge to the
density of the underlying random variate. Notice that the range of the
generated point set is always given by the range of the given sample.

   This method is provided in UNU.RAN for the sake of completeness. We always
recommend to use method EMPK (*note EMPirical distribution with Kernel
smoothing: EMPK.).

   If the data seem to be far away from having a bell shaped histogram, then we
think that naive resampling is still better than linear interpolation.

How To Use
..........

EMPL creates and samples from an empiral distribution by linear interpolation
of the empirical CDF. There are no parameters to set.

_Important_: We do not recommend to use this method! Use method EMPK (*note
EMPirical distribution with Kernel smoothing: EMPK.)  instead.

Function reference
------------------

 -- Function: UNUR_PAR* unur_empl_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

5.4.3 HIST  -  HISTogramm of empirical distribution
---------------------------------------------------

Required:
     histogram

Speed:
     Set-up: moderate, Sampling: fast

Reinit:
     not implemented


   Method HIST generates random variates from an empirical distribution that is
given as histogram. Sampling is done using the inversion method.

   If observed (raw) data are provided we recommend method EMPK (*note
EMPirical distribution with Kernel smoothing: EMPK.)  instead of compting a
histogram as this reduces information.

How To Use
..........

Method HIST uses empirical distributions that are given as a histgram. There
are no optional parameters.

Function reference
------------------

 -- Function: UNUR_PAR* unur_hist_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

5.5 Methods for continuous multivariate distributions
=====================================================

Overview of methods
-------------------

Methods for continuous multivariate distributions
sample with `unur_sample_vec'

NORTA: Requires rank correlation matrix and marginal distributions.
VNROU: Requires the PDF.
MVSTD: Generator for built-in standard distributions.
MVTDR: Requires PDF and gradiant of PDF.

5.5.1 MVSTD  -  MultiVariate continuous STandarD distributions
--------------------------------------------------------------

Required:
     standard distribution from UNU.RAN library (*note Standard distributions:
     Stddist.).

Speed:
     depends on distribution and generator

Reinit:
     supported


   MVSTD is a wrapper for special generators for multivariate continuous
standard distributions. It only works for distributions in the UNU.RAN library
of standard distributions (*note Standard distributions: Stddist.).  If a
distribution object is provided that is build from scratch, or if no special
generator for the given standard distribution is provided, the `NULL' pointer
is returned.

How To Use
..........

Create a distribution object for a standard distribution from the UNU.RAN
library (*note Standard distributions: Stddist.).

   Sampling from truncated distributions (which can be constructed by changing
the default domain of a distribution by means of
`unur_distr_cvec_set_domain_rect' call) is not possible.

   It is possible to change the parameters and the domain of the chosen
distribution and run `unur_reinit' to reinitialize the generator object.

Function reference
------------------

 -- Function: UNUR_PAR* unur_mvstd_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for new generator. It requires a distribution object
     for a multivariate continuous distribution from the UNU.RAN library of
     standard distributions (*note Standard distributions: Stddist.).  Using a
     truncated distribution is not possible.

5.5.2 MVTDR  -  Multi-Variate Transformed Density Rejection
-----------------------------------------------------------

Required:
     log-concave (log)PDF, gradient of (log)PDF

Optional:
     mode

Speed:
     Set-up: slow, Sampling: depends on dimension

Reinit:
     not implemented

Reference:
     [HLD04: Sect.11.3.4; Alg.11.15.]  [LJa98]



   MVTDR a multivariate version of the Transformed Density Rection (*note
TDR::) that works for log-concave densities.  For this method the domain of the
distribution is partitioned into cones with the mode (or the center) of the
distribution as their (common) vertex. The hat function is then constructed as
tangent planes of the transformed density in each of these cones. The
respective construction points lie on the central lines in the cones through
the vertex. The point is chosen such that the hat is minimal among all such
points (see the given references for more details).

   The cones are created by starting with the orthants of the reals space.
These are then iteratively split when the volume below the hat in such cones is
too large. Thus an increasing number of cones results in a better fitting hat
function.  Notice however, that the required number of cones increases
exponentially with the number of dimension.  Moreover, due to the construction
the rejection does not converge to 1 and remains strictly larger than 1.

   For distributions with bounded domains the cones are cut to pyramids that
cover the domain.

How To Use
..........

Create a multivariate generator object that contains the PDF and its gradient.
This object also should contain the mode of the distribution (or a point nearby
should be provided as center of the distribution).

   The method has three parameter to adjust the method for the given
distribution:

`stepsmin'
     Minimal number of iterations for splitting cones.  Notice that we start
     with 2^dim initial cones and that we arrive at 2^(dim+stepsmin) cones
     after these splits. So this number must be set with care. It can be set by
     a `unur_mvtdr_set_stepsmin' call.

`boundsplitting'
     Cones where the volume below the hat is relatively large (i.e. larger than
     the average volume over all cones times `boundsplitting' are further split.
     This parameter can set via a `unur_mvtdr_set_boundsplitting' call.

`maxcones'
     The maximum number of generated cones. When this number is reached, the
     initialization routine is stopped. Notice that the rejection constant can
     be still prohibitive large.  This parameter can set via a
     `unur_mvtdr_set_maxcones' call.


   Setting of these parameter can be quite tricky. The default settings lead to
hat functions where the volume below the hat is similar in each cone. However,
there might be some problems with distributions with higher correlations, since
then too few cones are created. Then it might be necessary to increase the
values for `stepsmin' and `maxcones' and to set `boundsplitting' to `0'.

   The number of cones and the total volume below the hat can be controlled
using the respective calls `unur_mvtdr_get_ncones' and `unur_mvtdr_get_hatvol'.
Notice, that the rejection constant is bounded from below by some figure
(larger than 1) that depends on the dimension.

   Unfortunately, the algorithm cannot detect the quality of the constructed
hat.

Function reference
------------------

 -- Function: UNUR_PAR* unur_mvtdr_new (const UNUR_DISTR* DISTRIBUTION)
     Get parameters for generator.

 -- Function: int unur_mvtdr_set_stepsmin (UNUR_PAR* PARAMETERS, int STEPSMIN)
     Set minimum number of triangulation step for each starting cone.  STEPSMIN
     must be nonnegative.

     Default: `5'.

 -- Function: int unur_mvtdr_set_boundsplitting (UNUR_PAR* PARAMETERS, double
          BOUNDSPLITTING)
     Set bound for splitting cones. All cones are split which have a volume
     below the hat that is greater than BOUND_SPLITTING times the average over
     all volumes. However, the number given by the `unur_mvtdr_set_maxcones' is
     not exceeded.  Notice that the later number is always reached if
     BOUND_SPLITTING is less than 1.

     Default: `1.5'

 -- Function: int unur_mvtdr_set_maxcones (UNUR_PAR* PARAMETERS, int MAXCONES)
     Set maximum number of cones.

     Notice that this number is always increased to 2^(dim+stepsmin) where dim
     is the dimension of the distribution object and stepsmin the given mimimum
     number of triangulation steps.

     Notice: For higher dimensions and/or higher correlations between the
     coordinates of the random vector the required number of cones can be very
     high. A too small maximum number of cones can lead to a very high
     rejection constant.

     Default: `10000'.

 -- Function: int unur_mvtdr_get_ncones (const UNUR_GEN* GENERATOR)
     Get the number of cones used for the hat function of the GENERATOR.  (In
     case of an error `0' is returned.)

 -- Function: double unur_mvtdr_get_hatvol (const UNUR_GEN* GENERATOR)
     Get the volume below the hat for the GENERATOR.  (In case of an error
     `UNUR_INFINITY' is returned.)

 -- Function: int unur_mvtdr_set_verify (UNUR_PAR* PARAMETERS, int VERIFY)
 -- Function: int unur_mvtdr_chg_verify (UNUR_GEN* GENERATOR, int VERIFY)
     Turn verifying of algorithm while sampling on/off.  If the condition
     squeeze(x) <= PDF(x) <= hat(x) is violated for some x then `unur_errno' is
     set to `UNUR_ERR_GEN_CONDITION'. However notice that this might happen due
     to round-off errors for a few values of x (less than 1%).

     Default is `FALSE'.

5.5.3 NORTA  -  NORmal To Anything
----------------------------------

Required:
     rank correlation matrix, marginal distributions

Speed:
     Set-up: slow, Sampling: depends on dimension

Reinit:
     not implemented

Reference:
     [HLD04: Sect.12.5.2; Alg.12.11.]



   NORTA (NORmal to anything) is a model to get random vectors with given
marginal distributions and rank correlation.

   *Important:* Notice that marginal distribution and (rank) correlation
structure do not uniquely define a multivariate distribution. Thus there are
many other (more or less sensible) models.

   In the NORTA model multinormal random variates with the given (Spearman's)
rank correlations are generated.  In a second step the (standard normal
distributed) marginal variates are transformed by means of the CDF of the
normal distribution to get uniform marginals. The resulting random vectors have
uniform marginals and the desired rank correlation between its components.
Such a random vector is called 'copula'.

   By means of the inverse CDF the uniform marginals are then transformed into
the target marginal distributions. This transformation does not change the rank
correlation.

   For the generation of the multinormal distribution the (Spearman's) rank
correlation matrix is transformed into the corresponding (Pearson) correlation
matrix. Samples from the resulting multinormal distribution are generated by
means of the Cholesky decomposition of the covariance matrix.

   It can happen that the desired rank correlation matrix is not feasible,
i.e., it cannot occur as rank correlation matrix of a multinormal distribution.
The resulting "covariance" matrix is not positive definite. In this case an
eigenvector correction method is used. Then all non-positive eigenvalues are
set to a small positive value and hence the rank correlation matrix of the
generated random vectors is "close" to the desired matrix.

How To Use
..........

Create a multivariate generator object and set marginal distributions using
`unur_distr_cvec_set_marginals', `unur_distr_cvec_set_marginal_array' , or
`unur_distr_cvec_set_marginal_list'.  (Do not use the corresponding calls for
the standard marginal distributions).

   When the domain of the multivariate distribution is set by of a
`unur_distr_cvec_set_domain_rect' call then the domain of each of the marginal
distributions is truncated by the respective coordinates of the given rectangle.

   If copulae are required (i.e. multivariate distributions with uniform
marginals) such a generator object can be created by means of
`unur_distr_copula' .

   There are no optional parameters for this method.

Function reference
------------------

 -- Function: UNUR_PAR* unur_norta_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

5.5.4 VNROU  -  Multivariate Naive Ratio-Of-Uniforms method
-----------------------------------------------------------

Required:
     PDF

Optional:
     mode, center, bounding rectangle for acceptance region

Speed:
     Set-up: fast or slow, Sampling: slow

Reinit:
     supported

Reference:
     [WGS91]



   VNROU is an implementation of the multivariate ratio-of-uniforms method
which uses a (minimal) bounding hyper-rectangle, see also *note
Ratio-of-Uniforms::.  It uses an additional parameter r that can be used for
adjusting the algorithm to the given distribution to improve performance and/or
to make this method applicable.  Larger values of r increase the class of
distributions for which the method works at the expense of higher rejection
constants. Moreover, this implementation uses the center mu of the distribution
(which is set to the mode or mean by default, see `unur_distr_cvec_get_center'
for details of its default values).

   The minimal bounding has then the coordinates

     v^+ = sup_(x) (f(x))^(1/r d+1),
     u^-_i = inf_(x_i) (x_i- mu_i) (f(x))^(r/r d+1),
     u^+_i = sup_(x_i) (x_i- mu_i) (f(x))^(r/r d+1),

where x_i is the i-th coordinate of point x; mu_i is the i-th coordinate of the
center mu.  d denotes the dimension of the distribution.  These bounds can
either be given directly, or are computed automatically by means of an
numerical routine by Hooke and Jeeves [HJa61] called direct search (see
`src/utils/hooke.c' for further references and details). Of course this
algorithm can fail, especially when this rectangle is not bounded.

   It is important to note that the algorithm works with PDF(x-center) instead
of PDF(x), i.e. the bounding rectangle has to be provided for PDF(x-center).
This is important as otherwise the acceptance region can become a very long and
skinny ellipsoid along a diagonal of the (huge) bounding rectangle.

   VNROU is based on the rejection method (*note Rejection::), and it is
important to note that the acceptance probability decreases exponentially with
dimension. Thus even for moderately many dimensions (e.g. 5) the number of
repetitions to get one random vector can be prohibitively large and the
algorithm seems to stay in an infinite loop.

How To Use
..........

For using the VNROU method UNU.RAN needs the PDF of the distribution.
Additionally, the parameter r can be set via a `unur_vnrou_set_r' call. Notice
that the acceptance probability decreases when r is increased. On the other
hand is is more unlikely that the bounding rectangle does not exist if r is
small.

   A bounding rectangle can be given by the `unur_vnrou_set_u' and
`unur_vnrou_set_v' calls.

   _Important:_ The bounding rectangle has to be provided for the function
PDF(x-center)!  Notice that `center' is the center of the given distribution,
see `unur_distr_cvec_set_center'.  If in doubt or if this value is not optimal,
it can be changed (overridden) by a `unur_distr_cvec_set_center' call.

   If the coordinates of the bounding rectangle are not provided by the user
then the minimal bounding rectangle is computed automatically.

   By means of `unur_vnrou_set_verify' and `unur_vnrou_chg_verify' one can run
the sampling algorithm in a checking mode, i.e., in every cycle of the
rejection loop it is checked whether the used rectangle indeed enclosed the
acceptance region of the distribution. When in doubt (e.g., when it is not
clear whether the numerical routine has worked correctly) this can be used to
run a small Monte Carlo study.

   *Important:* The rejection constant (i.e. the expected number of iterations
for generationg one random vector) can be extremely high, in particular when
the dimension is 4 or higher.  Then the algorithm will perform almost infinite
loops.  Thus it is recommended to read the volume below the hat function by
means of the `unur_vnrou_get_volumehat' call. The returned number divided by
the volume below the PDF (which is 1 in case of a normalized PDF) gives the
rejection constant.

   It is possible to change the parameters and the domain of the chosen
distribution and run `unur_reinit' to reinitialize the generator object.
Notice, that the coordinates of a bounding rectangle given by `unur_vnrou_set_u'
and `unur_vnrou_set_v' calls are used also when the generator is reused. These
can be changed by means of `unur_vnrou_chg_u' and `unur_vnrou_chg_v' calls.
(If no such coordinates have been given, then they are computed numerically
during the reinitialization proceedure.)

Function reference
------------------

 -- Function: UNUR_PAR* unur_vnrou_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_vnrou_set_u (UNUR_PAR* PARAMETERS, double* UMIN, double*
          UMAX)
     Sets left and right boundaries of bounding hyper-rectangle.  If no values
     are given, the boundary of the minimal bounding hyper-rectangle is
     computed numerically.

     *Important*: The boundaries are those of the density shifted by the center
     of the distribution, i.e., for the function PDF(x-center)!

     _Notice_: Computing the minimal bounding rectangle may fail under some
     circumstances. Moreover, for multimodal distributions the bounds might be
     too small as only local extrema are computed.  Nevertheless, for
     log-concave distributions it should work.

     Default: not set (i.e. computed automatically)

 -- Function: int unur_vnrou_chg_u (UNUR_GEN* GENERATOR, double* UMIN, double*
          UMAX)
     Change left and right boundaries of bounding hyper-rectangle.

 -- Function: int unur_vnrou_set_v (UNUR_PAR* PARAMETERS, double VMAX)
     Set upper boundary for bounding hyper-rectangle.  If no values are given,
     the density at the mode is evaluated.  If no mode is given for the
     distribution it is computed numerically (and might fail).

     Default: not set (i.e. computed automatically)

 -- Function: int unur_vnrou_chg_v (UNUR_GEN* GENERATOR, double VMAX)
     Change upper boundary for bounding hyper-rectangle.

 -- Function: int unur_vnrou_set_r (UNUR_PAR* PARAMETERS, double R)
     Sets the parameter R of the generalized multivariate ratio-of-uniforms
     method.

     _Notice_: This parameter must satisfy R>0.

     Default: `1'.

 -- Function: int unur_vnrou_set_verify (UNUR_PAR* PARAMETERS, int VERIFY)
     Turn verifying of algorithm while sampling on/off.

     If the condition PDF(x) <= hat(x) is violated for some x then `unur_errno'
     is set to `UNUR_ERR_GEN_CONDITION'. However notice that this might happen
     due to round-off errors for a few values of x (less than 1%).

     Default is `FALSE'.

 -- Function: int unur_vnrou_chg_verify (UNUR_GEN* GENERATOR, int VERIFY)
     Change the verifying of algorithm while sampling on/off.

 -- Function: double unur_vnrou_get_volumehat (const UNUR_GEN* GENERATOR)
     Get the volume of below the hat.  For normalized densities, i.e. when the
     volume below PDF is 1, this value equals the rejection constant for the
     vnrou method.

     In case of an error UNUR_INFINITY is returned.

5.6 Markov chain samplers for continuous multivariate distributions
===================================================================

Markov chain samplers generate sequences of random vectors which have the
target distribution as stationary distribution.  There generated vectors are
(more or less) correlated and it might take a long time until the sequence has
converged to the given target distribution.

   *Beware: MCMC sampling can be dangerous!*

Overview of methods
-------------------

Markov Chain Methods for continuous multivariate distributions
sample with `unur_sample_vec'

GIBBS: T-concave logPDF and derivatives of logPDF.
HITRO: Requires PDF.

5.6.1 GIBBS  -  Markov Chain - GIBBS sampler
--------------------------------------------

Required:
     T-concave logPDF, derivatives of logPDF

Speed:
     Set-up: fast, Sampling: moderate

Reinit:
     not implemented

Reference:
     [HLD04: Sect.14.1.2]



   Method GIBBS implements a Gibbs sampler for a multivariate distribution with
given joint density and its gradient.  When running such a Markov chain all
coordinates are updated cyclically using full conditional distributions. After
each step the state of the chain is returned (i.e., a random point is returned
whenever a single coordinate has been updated).  It is also possible to return
only points after all coordinates have been updated by "thinning" the chain.
Moreover, to reduce autocorrelation this thinning factor can be any integer.
Notice, however, that the sampling time for a chain of given length is
increased by the same factor, too.

   GIBBS also provides a variant of the Gibbs sampler where in each step a
point from the full conditional distribution along some random direction is
sampled. This direction is chosen uniformly from the sphere in each step.  This
method is also known as Hit-and-Run algorithm for non-uniform distributions.

   Our experiences shows that the original Gibbs sampler with sampling along
coordinate axes is superior to random direction sampling as long as the
correlations between the components of the random vector are not too high.

   For both variants transformed density rejection (see methods *note TDR:: and
*note ARS::) is used to sample from the full conditional distributions. In
opposition to the univariate case, it is important that the factor `c' is as
large as possible. I.e., for a log-concave density `c' must be set to `0.',
since otherwise numerical underflow might stop the algorithm.

   _Important:_ GIBBS does not generate independent random points. The starting
point of the Gibbs chain must be in a "typical" region of the target
distribution. If such a point is not known or would be too expensive, then the
first part of the chain should be discarded (burn-in of the chain).

How To Use
..........

For using the GIBBS method UNU.RAN needs the logarithm of the PDF of the
multivariate joint distribution and its gradient or partial derivatives.

   It provides two variants:
_coordinate direction sampling (Gibbs sampling) [default]_
     The coordinates are updated cyclically.  It requires the partial
     derivatives of the (logarithm of the) PDF of the target distribution, see
     `unur_distr_cvec_set_pdlogpdf'.  Otherwise, the gradient of the logPDF (see
     `unur_distr_cvec_set_dlogpdf' ) is used, which is more expensive.

     This variant can be selected using `unur_gibbs_set_variant_coordinate'.

_random direction sampling (nonuniform Hit-and-Run algorithm)_
     In each step is a direction is sampled uniformly from the sphere and the
     next point in the chain is sampled from the full conditional distribution
     along this direction.

     It requires the gradient of the logPDF and thus each step is more
     expensive than each step for coordinate direction sampling.

     This variant can be selected using
     `unur_gibbs_set_variant_random_direction'.

   It is important that the `c' parameter for the TDR method is as large as
possible. For logconcave distribution it must be set to `0', since otherwise
numerical underflow can cause the algorithm to stop.

   The starting point of the Gibbs chain must be "typical" for the target
distribution. If such a point is not known or would be too expensive, then the
first part of the chain should be discarded (burn-in of the chain). When using
the `unur_gibbs_set_burnin' call this is done during the setup of the Gibbs
sampler object.

   In case of a fatal error in the generator for conditional distributions the
methods generates points that contain UNUR_INFINITY.

   *Warning:* The algorithm requires that all full conditionals for the given
distribution object are T-concave. However, this property is not checked.  If
this property is not satisfied, then generation from the conditional
distributions becomes (very) slow and might fail or (even worse) produces
random vectors from an incorrect distribution.  When using
`unur_gibbs_set_burnin' then the setup already might fail. Thus when in doubt
whether GIBBS can be used for the targent distribution it is a good idea to use
a burn-in for checking.

   _Remark:_ It might happen (very rarely) that the chain becomes stuck due to
numerical errors. (This is in particular the case when the given PDF does not
fulfill the condition of this method.)  When this happens during burn-in then
the setup is aborted (i.e. it fails). Otherwise the chain restarts again from
its starting point.

   *Warning:* Be carefull with debugging flags. If it contains flag
`0x01000000u' it produces a lot of output for each step in the algorithm.
(This flag is switched of in the default debugging flags).

Function reference
------------------

 -- Function: UNUR_PAR* unur_gibbs_new (const UNUR_DISTR* DISTRIBUTION)
     ...........................................................................

 -- Function: int unur_gibbs_set_variant_coordinate (UNUR_PAR* PARAMETERS)
     Coordinate Direction Sampling: Sampling along the coordinate directions
     (cyclic).

     This is the default.

 -- Function: int unur_gibbs_set_variant_random_direction (UNUR_PAR* PARAMETERS)
     Random Direction Sampling: Sampling along the random directions.

 -- Function: int unur_gibbs_set_c (UNUR_PAR* PARAMETERS, double C)
     Set parameter C for transformation T of the transformed density rejection
     method.  Currently only values between `0' and `-0.5' are allowed. If `c'
     is between `0' and `-0.5' it is set to `-0.5'.

     For C `=0' (for logconcave densities) method ARS (*note ARS::) is used
     which is very robust against badly normalized PDFs. For other values
     method TDR (*note TDR::) is used.

     The value for C should be as large as possible to avoid fatal numerical
     underflows. Thus for log-concave distributions C must be set to `0.'

     Default is `0'.

 -- Function: int unur_gibbs_set_startingpoint (UNUR_PAR* PARAMETERS, const
          DOUBLE* X0)
     Sets the starting point of the Gibbs sampler. X0 must be a "typical" point
     of the given distribution.  If such a "typical" point is not known and a
     starting point is merely guessed, the first part of the Gibbs chain should
     be discarded (_burn-in_), e.g.\ by mean of the `unur_gibbs_set_burnin'
     call.

     Default is the result of `unur_distr_cvec_get_center' for the given
     distribution object.

 -- Function: int unur_gibbs_set_thinning (UNUR_PAR* PARAMETERS, int THINNING)
     Sets the THINNING parameter. When THINNING is set to k then every k-th
     point from the iteration is returned by the sampling algorithm.

     _Notice_: This parameter must satisfy THINNING>=1.

     Default: `1'.

 -- Function: int unur_gibbs_set_burnin (UNUR_PAR* PARAMETERS, int BURNIN)
     If a "typical" point for the target distribution is not known but merely
     guessed, the first part of the Gibbs chain should be discarded
     (_burn-in_). This can be done during the initialization of the generator
     object.  The length of the burn-in can is then BURNIN.

     When method GIBBS is not applicable for the target distribution then the
     initialization already might fail during the burn-in.  Thus this reduces
     the risk of running a generator that returns UNUR_INFINITY cased by some
     fatal error during sampling.

     The thinning factor set by a `unur_gibbs_set_thinning' call has no effect
     on the length of the burn-in, i.e., for the burn-in always a thinning
     factor `1' is used.

     _Notice_: This parameter must satisfy THINNING>=0.

     Default: `0'.

 -- Function: const double* unur_gibbs_get_state (UNUR_GEN* GENERATOR)
 -- Function: int unur_gibbs_chg_state (UNUR_GEN* GENERATOR, const DOUBLE*
          STATE)
     Get and change the current state of the Gibbs chain.

 -- Function: int unur_gibbs_reset_state (UNUR_GEN* GENERATOR)
     Reset state of chain to starting point.

     _Notice:_ Currently this function does not reset the generators for
     conditional distributions. Thus it is not possible to get the same Gibbs
     chain even when the underlying uniform random number generator is reset.

5.6.2 HITRO  -  Markov Chain - HIT-and-run sampler with Ratio-Of-uniforms
-------------------------------------------------------------------------

Required:
     PDF

Optional:
     mode, center, bounding rectangle for acceptance region

Speed:
     Set-up: fast, Sampling: fast

Reinit:
     not implemented

Reference:
     [KLPa05]



   HITRO is an implementation of a hit-and-run sampler that runs on the
acceptance region of the multivariate ratio-of-uniforms method, see *note
Ratio-of-Uniforms::.

   The Ratio-of-Uniforms transforms the region below the density into some
region that we call "region of acceptance" in the following. The minimal
bounding hyperrectangle of this region is given by

     v^+ = sup_(x) (f(x))^(1/r d+1),
     u^-_i = inf_(x_i) (x_i- mu_i) (f(x))^(r/r d+1),
     u^+_i = sup_(x_i) (x_i- mu_i) (f(x))^(r/r d+1),

where d denotes the dimension of the distribution; x_i is the i-th coordinate
of point x; mu_i is the i-th coordinate of the center mu of the distribution,
i.e., a point in the "main region" of the distribution.  Using the center is
important, since otherwise the acceptance region can become a very long and
skinny ellipsoid along a diagonal of the (huge) bounding rectangle.

   For each step of the Hit-and-Run algorithm we have to choose some direction.
This direction together with the current point of the chain determines a
straight line. Then a point is sampled uniformly on intersection of this line
and the region of acceptance. This is done by rejection from a uniform
distribution on a line segment that covers it.  Depending of the chosen variant
the endpoints of this covering line are computed either by means of a (not
necessary minimal) bounding hyper-rectangle, or just the "covering plate" of the
bounding hyper-rectangle.

   The required bounds of the hyper-rectable can be given directly by the user.
Otherwise, these are computed automatically by means of a numerical routine by
Hooke and Jeeves [HJa61] called direct search (see `src/utils/hooke.c' for
further references and details).  However, this expensive computation can be
avoided by determine these bounds "on the fly" by the following adaptive
algorithm: Start with some (small) hyper-rectangle and enlarge it whenever the
endpoints of the covering line segment are not contained in the acceptance
region of the Ratio-of-Unfiorms method.  This approach works reliable as long
as the region of acceptance is convex.

   The performance of the uniform sampling from the line segment is much
improved if the covering line is adjusted (shortened) whenever a point is
rejected (adaptive sampling). This technique reduces the expected number of
iterations enormously.

   Method HITRO requires that the region of acceptance of the Ratio-of-Uniforms
method is bounded. The shape of this region can be controlled by a parameter r.
Higher values of r result in larger classes of distributions with bounded region
of acceptance. (A distribution that has such a bounded region for some r also
has a bounded region for every r' greater than r.) On the other hand the
acceptance probability decreases with increasing r. Moreover, round-off errors
are more likely and (for large values of r) might result in a chain with a
stationary distribution different from the target distribution.

   Method HITRO works optimal for distributions whose region of acceptance is
convex. This is in particular the case for all log-concave distributions when
we set r = `1'.  For bounded but non-convex regions of acceptance convergence is
yet not guarenteed by mathematical theory.

How To Use
..........

Method HITRO requires the PDF of the target distribution (derivatives are not
necessary).

   The acceptance region of the Ratio-of-Uniforms transformation must be
bounded. Its shape is controlled by parameter r.  By default this parameter is
set to `1' as this guarentees a convex region of acceptance when the PDF of the
given distribution is log-concave. It should only be set to a different
(higher!) value using `unur_vnrou_set_r' if otherwise x_i (f(x))^(r/r d+1) were
not bounded for each coordinate.

   There are two variants of the HITRO sampler:
_coordinate direction sampling. [default]_
     The coordinates are updated cyclically.  This can be seen as a Gibbs
     sampler running on the acceptance region of the Ratio-of-Uniforms method.
     This variant can be selected using `unur_hitro_set_variant_coordinate'.

_random direction sampling._
     In each step is a direction is sampled uniformly from the sphere.

     This variant can be selected using
     `unur_hitro_set_variant_random_direction'.

   Notice that each iteration of the coordinate direction sampler is cheaper
than an iteration of the random direction sampler.

   Sampling uniformly from the line segment can be adjusted in several ways:

_Adaptive line sampling vs. simple rejection._
     When adaptive line sampling is switched on, the covering line is shortened
     whenever a point is rejected. However, when the region of acceptance is
     not convex the line segment from which we have to sample might not be
     connected. We found that the algorithm still works but at the time being
     there is no formal proof that the generated Markov chain has the required
     stationary distribution.

     Adaptive line sampling can switch on/off by means of the
     `unur_hitro_set_use_adaptiveline' call.

_Bounding hyper-rectangle vs. "covering plate"._
     For computing the covering line we can use the bounding hyper-rectangle or
     just its upper bound.  The latter saves computing time during the setup and
     when computing the covering during at each iteration step at the expense
     of a longer covering line. When adaptive line sampling is used the total
     generation time for the entire chain is shorter when only the "covering
     plate" is used.

     _Notice:_ When coordinate sampling is used the entire bounding rectangle
     is used.

     Using the entire bounding hyper-rectangle can be switched on/off by means
     of the `unur_hitro_set_use_boundingrectangle' call.

_Deterministic vs. adaptive bounding hyper-rectangle._
     A bounding rectangle can be given by the `unur_vnrou_set_u' and
     `unur_vnrou_set_v' calls.  Otherwise, the minimal bounding rectangle is
     computed automatically during the setup by means of a numerical algorithm.
     However, this is (very) slow especially in higher dimensions and it might
     happen that this algorithm (like any other numerical algorithm) does not
     return a correct result.

     Alternatively the bounding rectangle can be computed adaptively. In the
     latter case `unur_vnrou_set_u' and `unur_vnrou_set_v' can be used to
     provide a starting rectangle which must be sufficiently small.  Then both
     endpoints of the covering line segment are always check whether they are
     outside the acceptance region of the Ratio-of-Uniforms method. If they are
     not, then the line segment and the ("bounding") rectangle are enlarged
     using a factor that can be given using the
     `unur_hitro_set_adaptive_multiplier' call.

     Notice, that running this method in the adaptive rectangle mode requires
     that the region of acceptance is convex when random directions are used,
     or the given PDF is unimodal when coordinate direction sampling is used.
     Moreover, it requires two additional calls to the PDF in each iteration
     step of the chain.

     Using addaptive bounding rectangles can be switched on/off by means of the
     `unur_hitro_set_use_adaptiverectangle' call.


   The algorithm takes of a bounded rectangular domain given by a
`unur_distr_cvec_set_domain_rect' call, i.e. the PDF is set to zero for every x
outside the given domain.  However, it is only the coordinate direction sampler
where the boundary values are directly used to get the endpoins of the
coverline line for the line sampling step.

   _Important:_ The bounding rectangle has to be provided for the function
PDF(x-center)!  Notice that `center' is the center of the given distribution,
see `unur_distr_cvec_set_center'.  If in doubt or if this value is not optimal,
it can be changed (overridden) by a `unur_distr_cvec_set_center' call.

Function reference
------------------

 -- Function: UNUR_PAR* unur_hitro_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_hitro_set_variant_coordinate (UNUR_PAR* PARAMETERS)
     Coordinate Direction Sampling: Sampling along the coordinate directions
     (cyclic).

     _Notice:_ For this variant the entire bounding rectangle is always used
     independent of the `unur_hitro_set_use_boundingrectangle' call.

     This is the default.

 -- Function: int unur_hitro_set_variant_random_direction (UNUR_PAR* PARAMETERS)
     Random Direction Sampling: Sampling along the random directions.

 -- Function: int unur_hitro_set_use_adaptiveline (UNUR_PAR* PARAMETERS, int
          ADAPTIVE)
     When ADAPTIVE is set to `TRUE' adaptive line sampling is applied,
     otherwise simple rejection is used.

     _Notice:_ When adaptive line sampling is switched off, the entire bounding
     rectangle must be used since otherwise the sampling time can be
     arbitrarily slow.

     _Warning:_ When adaptive line sampling is switched off, sampling can be
     arbitrarily slow. In particular this happens when random direction
     sampling is used for distributions with rectangular domains. Then the
     algorithm can be trapped into a vertex (or even edge).

     Default is `TRUE'.

 -- Function: int unur_hitro_set_use_boundingrectangle (UNUR_PAR* PARAMETERS,
          int RECTANGLE)
     When RECTANGLE is set to `TRUE' the entire bounding rectangle is used for
     computing the covering line. Otherwise, only an upper bound for the
     acceptance region is used.

     _Notice:_ When coordinate sampling is used the entire bounding rectangle
     has is always used and this call has no effect.

     Default: `FALSE' for random direction samplig, `TRUE' for coordinate
     direction sampling.

 -- Function: int unur_hitro_set_use_adaptiverectangle (UNUR_PAR* PARAMETERS,
          int ADAPTIVE)
     When ADAPTIVE is set to `FALSE' the bounding rectangle is determined
     during the setup. Either, it is computed automatically by a (slow)
     numerical method, or it must be provided by `unur_vnrou_set_u' and
     `unur_vnrou_set_v' calls.

     If ADAPTIVE is set to `TRUE' the bounding rectangle is computed
     adaptively. In this case the `unur_vnrou_set_u' and `unur_vnrou_set_v'
     calls can be used to provide a starting rectangle. This should be
     sufficiently small.  If not given then we assume v_(max) = 1,
     u_(min)=(-0.001,-0.001,...,-0.001), and u_(max)=(0.001,0.001,...,0.001).
     Adaptive enlargements of the bounding hyperrectangle can be controlled set
     setting an enlargement factor given by a
     `unur_hitro_set_adaptive_multiplier' call.

     Using adaptive computation of the bounding rectangle reduces the setup
     time significantly (when it is not given by the user) at the expense of
     two additional PDF evaluations during each iteration step.

     _Important:_ Using adaptive bounding rectangles requires that the region
     of acceptance is convex when random directions are used, or a unimodal PDF
     when coordinate direction sampling is used.

     Default: `FALSE' for random direction samplig, `TRUE' for coordinate
     direction sampling.

 -- Function: int unur_hitro_set_r (UNUR_PAR* PARAMETERS, double R)
     Sets the parameter R of the generalized multivariate ratio-of-uniforms
     method.

     _Notice_: This parameter must satisfy R>0.

     Default: `1'.

 -- Function: int unur_hitro_set_v (UNUR_PAR* PARAMETERS, double VMAX)
     Set upper boundary for bounding hyper-rectangle.  If not set not set the
     mode of the distribution is used.

     If adaptive bounding rectangles the value is used for the starting
     rectangle. If not given (and the mode of the distribution is not known)
     then VMAX=`1e-3' is used.

     If deterministic bounding rectangles these values are the given values are
     used for the rectangle. If no value is given (and the mode of the
     distribution is not known), the upper bound of the minimal bounding
     hyper-rectangle is computed numerically (slow).

     Default: not set.

 -- Function: int unur_hitro_set_u (UNUR_PAR* PARAMETERS, const DOUBLE* UMIN,
          const DOUBLE* UMAX)
     Sets left and right boundaries of bounding hyper-rectangle.

     If adaptive bounding rectangles these values are used for the starting
     rectangle. If not given then UMIN=`{-b,-b,...,-b}' and UMAX=`{b,b,...,b}'
     with `b=1.e-3' is used.

     If deterministic bounding rectangles these values are the given values are
     used for the rectangle. If no values are given, the boundary of the
     minimal bounding hyper-rectangle is computed numerically (slow).

     *Important*: The boundaries are those of the density shifted by the center
     of the distribution, i.e., for the function PDF(x-center)!

     _Notice_: Computing the minimal bounding rectangle may fail under some
     circumstances. Moreover, for multimodal distributions the bounds might be
     too small as only local extrema are computed.  Nevertheless, for
     log-concave distributions it should work.

     Default: not set.

 -- Function: int unur_hitro_set_adaptive_multiplier (UNUR_PAR* PARAMETERS,
          double FACTOR)
     Adaptive enlargements of the bounding hyperrectangle can be controlled set
     setting the enlargement FACTOR.  This must be greater than 1. Values close
     to 1 result in small adaptive steps and thus reduce the risk of too large
     bounding rectangles. On the other hand many adaptive steps might be
     necessary.

     _Notice:_ For practical reasons this call does not accept values for
     FACTOR less than `1.0001'. If this value is UNUR_INFINITY this results in
     infinite loops.

     Default: `1.1'

 -- Function: int unur_hitro_set_startingpoint (UNUR_PAR* PARAMETERS, const
          DOUBLE* X0)
     Sets the starting point of the HITRO sampler in the original scale. X0
     must be a "typical" point of the given distribution.  If such a "typical"
     point is not known and a starting point is merely guessed, the first part
     of the HITRO chain should be discarded (_burn-in_), e.g.\ by mean of the
     `unur_hitro_set_burnin' call.

     _Important:_ The PDF of the distribution must not vanish at the given
     point X0.

     Default is the result of `unur_distr_cvec_get_center' for the given
     distribution object.

 -- Function: int unur_hitro_set_thinning (UNUR_PAR* PARAMETERS, int THINNING)
     Sets the THINNING parameter. When THINNING is set to k then every k-th
     point from the iteration is returned by the sampling algorithm.  If
     thinning has to be set such that each coordinate is updated when using
     coordinate direction sampling, then THINNING should be `dim+1' (or any
     multiple of it) where `dim' is the dimension of the distribution object.

     _Notice_: This parameter must satisfy THINNING>=1.

     Default: `1'.

 -- Function: int unur_hitro_set_burnin (UNUR_PAR* PARAMETERS, int BURNIN)
     If a "typical" point for the target distribution is not known but merely
     guessed, the first part of the HITRO chain should be discarded
     (_burn-in_). This can be done during the initialization of the generator
     object.  The length of the burn-in can is then BURNIN.

     The thinning factor set by a `unur_hitro_set_thinning' call has no effect
     on the length of the burn-in, i.e., for the burn-in always a thinning
     factor `1' is used.

     _Notice_: This parameter must satisfy THINNING>=0.

     Default: `0'.

 -- Function: const double* unur_hitro_get_state (UNUR_GEN* GENERATOR)
 -- Function: int unur_hitro_chg_state (UNUR_GEN* GENERATOR, const DOUBLE*
          STATE)
     Get and change the current state of the HITRO chain.

     _Notice:_ The state variable contains the point in the `dim+1' dimensional
     point in the (tansformed) region of acceptance of the Ratio-of-Uniforms
     method. Its coordinate are stored in the following order: `state[] = {v,
     u1, u2, ..., udim}'.

     If the state can only be changed if the given STATE is inside this region.

 -- Function: int unur_hitro_reset_state (UNUR_GEN* GENERATOR)
     Reset state of chain to starting point.

     _Notice:_ Currently this function does not reset the generators for
     conditional distributions. Thus it is not possible to get the same HITRO
     chain even when the underlying uniform random number generator is reset.

5.7 Methods for continuous empirical multivariate distributions
===============================================================

Overview of methods
-------------------

Methods for continuous empirical multivariate distributions
sample with `unur_sample_vec'

VEMPK: Requires an observed sample.

Example
-------

     /* ------------------------------------------------------------- */
     /* File: example_vemp.c                                          */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */

     /* Example how to sample from an empirial continuous             */
     /* multivariate distribution.                                    */

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;

       /* 4 data points of dimension 2                                */
       double data[] = { 1. ,1.,       /* 1st data point              */
     		    -1.,1.,       /* 2nd data point              */
     		    1.,-1.,       /* 3rd data point              */
     		    -1.,-1. };    /* 4th data point              */

       double result[2];

       /* Declare the three UNURAN objects.                           */
       UNUR_DISTR *distr;    /* distribution object                   */
       UNUR_PAR   *par;      /* parameter object                      */
       UNUR_GEN   *gen;      /* generator object                      */

       /* Create a distribution object with dimension 2.              */
       distr = unur_distr_cvemp_new( 2 );

       /* Set empirical sample.                                       */
       unur_distr_cvemp_set_data(distr, data, 4);

       /* Choose a method: VEMPK.                                     */
       par = unur_vempk_new(distr);

       /* Use variance correction.                                    */
       unur_vempk_set_varcor( par, 1 );

       /* Create the generator object.                                */
       gen = unur_init(par);

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* It is possible to reuse the distribution object to create   */
       /* another generator object. If you do not need it any more,   */
       /* it should be destroyed to free memory.                      */
       unur_distr_free(distr);

       /* Now you can use the generator object `gen' to sample from   */
       /* the distribution. Eg.:                                      */
       for (i=0; i<10; i++) {
         unur_sample_vec(gen, result);
         printf("(%f,%f)\n", result[0], result[1]);
       }

       /* When you do not need the generator object any more, you     */
       /* can destroy it.                                             */
       unur_free(gen);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

Example (String API)
--------------------

(not implemented)

5.7.1 VEMPK  -  (Vector) EMPirical distribution with Kernel smoothing
---------------------------------------------------------------------

Required:
     observed sample

Speed:
     Set-up: slow, Sampling: slow (depends on dimension)

Reinit:
     not implemented

Reference:
     [HLa00] [HLD04: Sect.12.2.1]



   VEMPK generates random variates from a multivariate empirical distribution
that is given by an observed sample. The idea is that simply choosing a random
point from the sample and to return it with some added noise results in a
method that has very nice properties, as it can be seen as sampling from a
kernel density estimate.  Clearly we have to decide about the density of the
noise (called kernel) and about the covariance matrix of the noise.  The
mathematical theory of kernel density estimation shows us that we are
comparatively free in choosing the kernel.  It also supplies us with a simple
formula to compute the optimal standarddeviation of the noise, called bandwidth
(or window width) of the kernel.

   Currently only a Gaussian kernel with the same covariance matrix as the
given sample is implemented.  However it is possible to choose between a
variance corrected version or those with optimal MISE.  Additionally a
smoothing factor can be set to adjust the estimated density to non-bell-shaped
data densities.

How To Use
..........

VEMPK uses empirical distributions. The main parameter would be the choice if
of kernel density. However, currently only Gaussian kernels are supported. The
parameters for the density are computed by a simple but robust method. However,
it is possible to control its behavior by changing the smoothing factor.
Additionally, variance correction can be swithed on (at the price of suboptimal
MISE).

Function reference
------------------

 -- Function: UNUR_PAR* unur_vempk_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_vempk_set_smoothing (UNUR_PAR* PARAMETERS, double
          SMOOTHING)
 -- Function: int unur_vempk_chg_smoothing (UNUR_GEN* GENERATOR, double
          SMOOTHING)
     Set and change the smoothing factor.  The smoothing factor controlles how
     "smooth" the resulting density estimation will be. A smoothing factor
     equal to 0 results in naive resampling. A very large smoothing factor
     (together with the variance correction) results in a density which is
     approximately equal to the kernel.  Default is 1 which results in a
     smoothing parameter minimising the MISE (mean integrated squared error) if
     the data are not too far away from normal. If a large smoothing factor is
     used, then variance correction must be switched on.

     Default: `1'

 -- Function: int unur_vempk_set_varcor (UNUR_PAR* PARAMETERS, int VARCOR)
 -- Function: int unur_vempk_chg_varcor (UNUR_GEN* GENERATOR, int VARCOR)
     Switch variance correction in generator on/off.  If VARCOR is `TRUE' then
     the variance of the used density estimation is the same as the sample
     variance. However this increases the MISE of the estimation a little bit.

     Default is `FALSE'.

5.8 Methods for discrete univariate distributions
=================================================

Overview of methods
-------------------

Methods for discrete univariate distributions
sample with `unur_sample_discr'

method   PMF   PV   mode   sum    other
DARI     x          x      ~      T-concave
DAU      [x]   x                  
DGT      [x]   x                  
DSROU    x          x      x      T-concave
DSS      [x]   x           x      
DSTD                              build-in standard distribution
CEXT                              wrapper for external generator

Example
-------

     /* ------------------------------------------------------------- */
     /* File: example_discr.c                                         */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */

     /* Example how to sample from a discrete univariate distribution.*/

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;
       double param = 0.3;

       double probvec[10] = {1.0, 2.0, 3.0, 4.0, 5.0,\
                             6.0, 7.0, 8.0, 4.0, 3.0};

       /* Declare the three UNURAN objects.                           */
       UNUR_DISTR *distr1, *distr2;    /* distribution objects        */
       UNUR_PAR   *par1, *par2;        /* parameter objects           */
       UNUR_GEN   *gen1, *gen2;        /* generator objects           */

       /* First distribution: defined by PMF.                         */
       distr1 = unur_distr_geometric(&param, 1);
       unur_distr_discr_set_mode(distr1, 0);

       /* Choose a method: DARI.                                      */
       par1 = unur_dari_new(distr1);
       gen1 = unur_init(par1);

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen1 == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* Second distribution: defined by (finite) PV.                */
       distr2 = unur_distr_discr_new();
       unur_distr_discr_set_pv(distr2, probvec, 10);

       /* Choose a method: DGT.                                       */
       par2 = unur_dgt_new(distr2);
       gen2 = unur_init(par2);
       if (gen2 == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* print some random integers                                  */
       for (i=0; i<10; i++){
         printf("number %d: %d\n", i*2,   unur_sample_discr(gen1) );
         printf("number %d: %d\n", i*2+1, unur_sample_discr(gen2) );
       }

       /* Destroy all objects.                                        */
       unur_distr_free(distr1);
       unur_distr_free(distr2);
       unur_free(gen1);
       unur_free(gen2);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

Example (String API)
--------------------

     /* ------------------------------------------------------------- */
     /* File: example_discr_str.c                                     */
     /* ------------------------------------------------------------- */
     /* String API.                                                   */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */

     /* Example how to sample from a discrete univariate distribution.*/

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;     /* loop variable                                 */

       /* Declare UNURAN generator objects.                           */
       UNUR_GEN   *gen1, *gen2;        /* generator objects           */


       /* First distribution: defined by PMF.                         */
       gen1 = unur_str2gen("geometric(0.3); mode=0 & method=dari");

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen1 == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* Second distribution: defined by (finite) PV.                */
       gen2 = unur_str2gen(
                  "distr=discr; pv=(1,2,3,4,5,6,7,8,4,3) & method=dgt");
       if (gen2 == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* print some random integers                                  */
       for (i=0; i<10; i++){
         printf("number %d: %d\n", i*2,   unur_sample_discr(gen1) );
         printf("number %d: %d\n", i*2+1, unur_sample_discr(gen2) );
       }

       /* Destroy all objects.                                        */
       unur_free(gen1);
       unur_free(gen2);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

5.8.1 DARI  -  Discrete Automatic Rejection Inversion
-----------------------------------------------------

Required:
     T-concave PMF, mode, approximate area

Speed:
     Set-up: moderate, Sampling: fast

Reinit:
     supported

Reference:
     [HDa96] [HLD04: Sect.10.2; Alg.10.4]



   DARI is based on rejection inversion, which can be seen as an adaptation of
transformed density rejection to discrete distributions. The used
transformation is -1/sqrt(x) .

   DARI uses three almost optimal points for constructing the (continuous) hat.
Rejection is then done in horizontal direction. Rejection inversion uses only
one uniform random variate per trial.

   DARI has moderate set-up times (the PMF is evaluated nine times), and good
marginal speed, especially if an auxiliary array is used to store values during
generation.

   DARI works for all T_(-1/2) -concave distributions. It requires the PMF and
the location of the mode. Moreover the approximate sum over the PMF is used.
(If no sum is given for the distribution the algorithm assumes that it is
approximately 1.)  The rejection constant is bounded from above by 4 for all
T-concave distributions.

How To Use
..........

DARI works for discrete distribution object with given PMF.  The sum over
probabilities should be approximately one. Otherwise it must be set by a
`unur_distr_discr_set_pmfsum' call to its (approximate) value.

   The size of an auxiliary table can be set by `unur_dari_set_tablesize'.  The
expected number of evaluations can be reduced by switching the use of squeezes
by means of `unur_dari_set_squeeze'.  It is possible to change the parameters
and the domain of the chosen distribution and run `unur_reinit' to reinitialize
the generator object.  Notice, that derived parameters like the mode must also
be (re-) set if the parameters or the domain has be changed.

   There exists a test mode that verifies whether the conditions for the method
are satisfied or not. It can be switched on by calling `unur_dari_set_verify'
and `unur_dari_chg_verify', respectively.  Notice however that sampling is
(much) slower then.

Function reference
------------------

 -- Function: UNUR_PAR* unur_dari_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_dari_set_squeeze (UNUR_PAR* PARAMETERS, int SQUEEZE)
     Turn utilization of the squeeze of the algorithm on/off.  This squeeze
     does not resamble the squeeze of the continuous TDR method. It was
     especially designed for rejection inversion.

     The squeeze is not necessary if the size of the auxiliary table is big
     enough (for the given distribution).  Using a squeeze is suggested to
     speed up the algorithm if the domain of the distribution is very big or if
     only small samples are produced.

     Default: no squeeze.

 -- Function: int unur_dari_set_tablesize (UNUR_PAR* PARAMETERS, int SIZE)
     Set the size for the auxiliary table, that stores constants computed
     during generation.  If SIZE is set to `0' no table is used.  The speed-up
     can be impressive if the PMF is expensive to evaluate and the "main part
     of the distribution" is concentrated in an interval shorter than the size
     of the table.

     Default is `100'.

 -- Function: int unur_dari_set_cpfactor (UNUR_PAR* PARAMETERS, double
          CP_FACTOR)
     Set factor for position of the left and right construction point, resp.
     The CP_FACTOR is used to find almost optimal construction points for the
     hat function.  The CP_FACTOR must be positive and should not exceed 2.
     There is no need to change this factor in almost all situations.

     Default is `0.664'.

 -- Function: int unur_dari_set_verify (UNUR_PAR* PARAMETERS, int VERIFY)
 -- Function: int unur_dari_chg_verify (UNUR_GEN* GENERATOR, int VERIFY)
     Turn verifying of algorithm while sampling on/off.  If the condition is
     violated for some x then `unur_errno' is set to `UNUR_ERR_GEN_CONDITION'.
     However notice that this might happen due to round-off errors for a few
     values of x (less than 1%).

     Default is `FALSE'.

5.8.2 DAU  -  (Discrete) Alias-Urn method
-----------------------------------------

Required:
     probability vector (PV)

Speed:
     Set-up: slow (linear with the vector-length), Sampling: very fast

Reinit:
     supported

Reference:
     [WAa77] [HLD04: Sect.3.2]



   DAU samples from distributions with arbitrary but finite probability vectors
(PV) of length N.  The algorithmus is based on an ingeneous method by A.J.
Walker and requires a table of size (at least) N.  It needs one random numbers
and only one comparison for each generated random variate. The setup time for
constructing the tables is O(N).

   By default the probability vector is indexed starting at `0'. However this
can be changed in the distribution object by a `unur_distr_discr_set_domain'
call.

   The method also works when no probability vector but a PMF is given. However
then additionally a bounded (not too large) domain must be given or the sum
over the PMF (see `unur_distr_discr_make_pv' for details).

How To Use
..........

Create an object for a discrete distribution either by setting a probability
vector or a PMF. The performance can be slightly influenced by setting the size
of the used table which can be changed by `unur_dau_set_urnfactor'.  It is
possible to change the parameters and the domain of the chosen distribution and
run `unur_reinit' to reinitialize the generator object.

Function reference
------------------

 -- Function: UNUR_PAR* unur_dau_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_dau_set_urnfactor (UNUR_PAR* PARAMETERS, double FACTOR)
     Set size of urn table relative to length of the probability vector. It
     must not be less than 1. Larger tables result in (slightly) faster
     generation times but require a more expensive setup. However sizes larger
     than 2 are not recommended.

     Default is `1'.

5.8.3 DEXT  -  wrapper for Discrete EXTernal generators
-------------------------------------------------------

Required:
     routine for sampling discrete random variates

Speed:
     depends on external generator

Reinit:
     supported


   Method DEXT is a wrapper for external generators for discrete univariate
distributions. It allows the usage of external random variate generators within
the UNU.RAN framework.

How To Use
..........

The following steps are required to use some external generator within the
UNU.RAN framework (some of these are optional):

  1. Make an empty generator object using a `unur_dext_new' call.  The argument
     DISTRIBUTION is optional and can be replaced by `NULL'. However, it is
     required if you want to pass parameters of the generated distribution to
     the external generator or for running some validation tests provided by
     UNU.RAN.

  2. Create an initialization routine of type `int (*init)(UNUR_GEN *gen)' and
     plug it into the generator object using the `unur_dext_set_init' call.
     Notice that the INIT routine must return `UNUR_SUCCESS' when it has been
     executed successfully and `UNUR_FAILURE' otherwise.  It is possible to get
     the size of and the pointer to the array of parameters of the underlying
     distribution object by the respective calls `unur_dext_get_ndistrparams'
     and `unur_dext_get_distrparams'.  Parameters for the external generator
     that are computed in the INIT routine can be stored in a single array or
     structure which is available by the `unur_dext_get_params' call.

     Using an INIT routine is optional and can be omitted.

  3. Create a sampling routine of type `int (*sample)(UNUR_GEN *gen)' and plug
     it into the generator object using the `unur_dext_set_sample' call.

     Uniform random numbers are provided by the `unur_sample_urng' call. Do not
     use your own implementation of a uniform random number generator directly.
     If you want to use your own random number generator we recommend to use
     the UNU.RAN interface (see *note Using uniform random number generators:
     URNG.).

     The array or structure that contains parameters for the external generator
     that are computed in the INIT routine are available using the
     `unur_dext_get_params' call.

     Using a SAMPLE routine is of course obligatory.

   It is possible to change the parameters and the domain of the chosen
distribution and run `unur_reinit' to reinitialize the generator object. The
INIT routine is then called again.

   Here is a short example that demonstrates the application of this method by
means of the geometric distribution:

     /* ------------------------------------------------------------- */
     /* File: example_dext.c                                          */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */

     /* This example shows how an external generator for the          */
     /* geometric distribution can be used within the UNURAN          */
     /* framework.                                                    */
     /*                                                               */
     /* Notice, that this example does not provide the simplest       */
     /* solution.                                                     */

     /* ------------------------------------------------------------- */
     /* Initialization routine.                                       */
     /*                                                               */
     /*   Here we simply read the parameter of the geometric          */
     /*   distribution and store it in an array for parameters of     */
     /*   the external generator.                                     */
     /*   [ Of course we could do this in the sampling routine as     */
     /*   and avoid the necessity of this initialization routine. ]   */

     int geometric_init (UNUR_GEN *gen)
     {
       /* Get pointer to parameters of geometric distribution         */
       double *params = unur_dext_get_distrparams(gen);

       /* The parameter is the first entry (see manual)               */
       double p = params[0];

       /* Get array to store this parameter for external generator    */
       double *genpar = unur_dext_get_params(gen, sizeof(double));
       genpar[0] = p;

       /* Executed successfully                                       */
       return UNUR_SUCCESS;
     }

     /* ------------------------------------------------------------- */
     /* Sampling routine.                                             */
     /*                                                               */
     /*   Contains the code for the external generator.               */

     int geometric_sample (UNUR_GEN *gen)
     {
       /* Get scale parameter                                         */
       double *genpar = unur_dext_get_params(gen,0);
       double p = genpar[0];

       /* Sample a uniformly distributed random number                */
       double U = unur_sample_urng(gen);

       /* Transform into geometrically distributed random variate     */
       return ( (int) (log(U) / log(1.-p)) );
     }

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int i;     /* loop variable                                    */
       int K;     /* will hold the random number                      */

       /* Declare the three UNURAN objects.                           */
       UNUR_DISTR *distr;    /* distribution object                   */
       UNUR_PAR   *par;      /* parameter object                      */
       UNUR_GEN   *gen;      /* generator object                      */

       /* Use predefined geometric distribution with parameter 1/10   */
       double fpar[1] = { 0.1 };
       distr = unur_distr_geometric(fpar, 1);

       /* Use method DEXT                                             */
       par = unur_dext_new(distr);

       /* Set initialization and sampling routines.                   */
       unur_dext_set_init(par, geometric_init);
       unur_dext_set_sample(par, geometric_sample);

       /* Create the generator object.                                */
       gen = unur_init(par);

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* It is possible to reuse the distribution object to create   */
       /* another generator object. If you do not need it any more,   */
       /* it should be destroyed to free memory.                      */
       unur_distr_free(distr);

       /* Now you can use the generator object `gen' to sample from   */
       /* the standard Gaussian distribution.                         */
       /* Eg.:                                                        */
       for (i=0; i<10; i++) {
         K = unur_sample_discr(gen);
         printf("%d\n",K);
       }

       /* When you do not need the generator object any more, you     */
       /* can destroy it.                                             */
       unur_free(gen);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

Function reference
------------------

 -- Function: UNUR_PAR* unur_dext_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for new generator.

 -- Function: int unur_dext_set_init (UNUR_PAR* PARAMETERS, int (*
          INIT)(UNUR_GEN* gen ))
     Set initialization routine for external generator. Inside the

     _Important:_ The routine INIT must return `UNUR_SUCCESS' when the
     generator was initialized successfully and `UNUR_FAILURE' otherwise.

     Parameters that are computed in the INIT routine can be stored in an array
     or structure that is avaiable by means of the `unur_dext_get_params' call.
     Parameters of the underlying distribution object can be obtained by the
     `unur_dext_get_distrparams' call.

 -- Function: int unur_dext_set_sample (UNUR_PAR* PARAMETERS, int (*
          SAMPLE)(UNUR_GEN* gen ))
     Set sampling routine for external generator.

     _Important:_ Use `unur_sample_urng(gen)' to get a uniform random number.
     The pointer to the array or structure that contains the parameters that
     are precomputed in the INIT routine are available by
     `unur_dext_get_params(gen,0)'.  Additionally one can use the
     `unur_dext_get_distrparams' call.

 -- Function: void* unur_dext_get_params (UNUR_GEN* GENERATOR, size_t SIZE)
     Get pointer to memory block for storing parameters of external generator.
     A memory block of size SIZE is automatically (re-) allocated if necessary
     and the pointer to this block is stored in the GENERATOR object. If one
     only needs the pointer to this memory block set SIZE to `0'.

     Notice, that SIZE is the size of the memory block and not the length of an
     array.

     _Important:_ This rountine should only be used in the initialization and
     sampling routine of the external generator.

 -- Function: double* unur_dext_get_distrparams (UNUR_GEN* GENERATOR)
 -- Function: int unur_dext_get_ndistrparams (UNUR_GEN* GENERATOR)
     Get size of and pointer to array of parameters of underlying distribution
     in GENERATOR object.

     _Important:_ These rountines should only be used in the initialization and
     sampling routine of the external generator.

5.8.4 DGT  -  (Discrete) Guide Table method (indexed search)
------------------------------------------------------------

Required:
     probability vector (PV)

Speed:
     Set-up: slow (linear with the vector-length), Sampling: very fast

Reinit:
     supported

Reference:
     [CAa74] [HLD04: Sect.3.1.2]



   DGT samples from arbitrary but finite probability vectors. Random numbers
are generated by the inversion method, i.e.,

  1. Generate a random number U ~ U(0,1).

  2. Find smallest integer I such that F(I) = P(X<=I) >= U.

   Step (2) is the crucial step. Using sequential search requires O(E(X))
comparisons, where E(X) is the expectation of the distribution. Indexed search,
however, uses a guide table to jump to some I' <= I near I to find X in constant
time. Indeed the expected number of comparisons is reduced to 2, when the guide
table has the same size as the probability vector (this is the default). For
larger guide tables this number becomes smaller (but is always larger than 1),
for smaller tables it becomes larger. For the limit case of table size 1 the
algorithm simply does sequential search (but uses a more expensive setup then
method DSS (*note DSS::). On the other hand the setup time for guide table is
O(N), where N denotes the length of the probability vector (for size 1 no
preprocessing is required). Moreover, for very large guide tables memory
effects might even reduce the speed of the algorithm. So we do not recommend to
use guide tables that are more than three times larger than the given
probability vector. If only a few random numbers have to be generated, (much)
smaller table sizes are better.  The size of the guide table relative to the
length of the given probability vector can be set by a
`unur_dgt_set_guidefactor' call.

   There exist two variants for the setup step which can be set by a
`unur_dgt_set_variant' call: Variants 1 and 2.  Variant 2 is faster but more
sensitive to roundoff errors when the guide table is large. By default variant
2 is used for short probability vectors (N<1000) and variant 1 otherwise.

   By default the probability vector is indexed starting at `0'. However this
can be changed in the distribution object by a `unur_distr_discr_set_domain'
call.

   The method also works when no probability vector but a PMF is given.
However, then additionally a bounded (not too large) domain must be given or
the sum over the PMF. In the latter case the domain of the distribution is
trucated (see `unur_distr_discr_make_pv' for details).

How To Use
..........

Create an object for a discrete distribution either by setting a probability
vector or a PMF. The performance can be slightly influenced by setting the size
of the used table which can be changed by `unur_dgt_set_guidefactor'.  It is
possible to change the parameters and the domain of the chosen distribution and
run `unur_reinit' to reinitialize the generator object.

Function reference
------------------

 -- Function: UNUR_PAR* unur_dgt_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_dgt_set_guidefactor (UNUR_PAR* PARAMETERS, double FACTOR)
     Set size of guide table relative to length of PV.  Larger guide tables
     result in faster generation time but require a more expensive setup. Sizes
     larger than 3 are not recommended.  If the relative size is set to 0,
     sequential search is used.  However, this is not recommended, except in
     exceptional cases, since method DSS (*note DSS::) is has almost no setup
     and is thus faster (but requires the sum over the PV as input parameter).

     Default is `1'.

 -- Function: int unur_dgt_set_variant (UNUR_PAR* PARAMETERS, unsigned VARIANT)
     Set variant for setup step. Possible values are `1' or `2'.  Variant `2'
     is faster but more sensitive to roundoff errors when the guide table is
     large.  By default variant `2' is used for short probability vectors
     (N<1000) and variant `1' otherwise.

5.8.5 DSROU  -  Discrete Simple Ratio-Of-Uniforms method
--------------------------------------------------------

Required:
     T-concave PMF, mode, sum over PMF

Speed:
     Set-up: fast, Sampling: slow

Reinit:
     supported

Reference:
     [LJa01] [HLD04: Sect.10.3.2; Alg.10.6]



   DSROU is based on the ratio-of-uniforms method (*note Ratio-of-Uniforms::)
but uses universal inequalities for constructing a (universal) bounding
rectangle.  It works for all T-concave distributions with T(x) = -1/sqrt(x) .

   The method requires the PMF, the (exact) location of the mode and the sum
over the given PDF. The rejection constant is 4 for all T-concave
distributions. Optionally the CDF at the mode can be given to increase the
performance of the algorithm. Then the rejection constant is reduced to 2.

How To Use
..........

The method works for T-concave discrete distributions with given PMF. The sum
over of the PMF or an upper bound of this sum must be known.

   Optionally the CDF at the mode can be given to increase the performance using
`unur_dsrou_set_cdfatmode'.  However, this *must not* be called if the sum over
the PMF is replaced by an upper bound.

   It is possible to change the parameters and the domain of the chosen
distribution and run `unur_reinit' to reinitialize the generator object.

   If any of mode, CDF at mode, or the sum over the PMF has been changed, then
`unur_reinit' must be executed.  (Otherwise the generator produces garbage).

   There exists a test mode that verifies whether the conditions for the method
are satisfied or not while sampling. It can be switched on or off by calling
`unur_dsrou_set_verify' and `unur_dsrou_chg_verify', respectively.  Notice
however that sampling is (a little bit) slower then.

Function reference
------------------

 -- Function: UNUR_PAR* unur_dsrou_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_dsrou_set_cdfatmode (UNUR_PAR* PARAMETERS, double FMODE)
     Set CDF at mode.  When set, the performance of the algorithm is increased
     by factor 2.  However, when the parameters of the distribution are changed
     `unur_dsrou_chg_cdfatmode' has to be used to update this value.  Notice
     that the algorithm detects a mode at the left boundary of the domain
     automatically and it is not necessary to use this call for a monotonically
     decreasing PMF.

     Default: not set.

 -- Function: int unur_dsrou_set_verify (UNUR_PAR* PARAMETERS, int VERIFY)
 -- Function: int unur_dsrou_chg_verify (UNUR_GEN* GENERATOR, int VERIFY)
     Turn verifying of algorithm while sampling on/off.  If the condition
     squeeze(x) <= PMF(x) <= hat(x) is violated for some x then `unur_errno' is
     set to `UNUR_ERR_GEN_CONDITION'. However notice that this might happen due
     to round-off errors for a few values of x (less than 1%).

     Default is `FALSE'.

 -- Function: int unur_dsrou_chg_cdfatmode (UNUR_GEN* GENERATOR, double FMODE)
     Change CDF at mode of distribution.  `unur_reinit' must be executed before
     sampling from the generator again.

5.8.6 DSS  -  (Discrete) Sequential Search method
-------------------------------------------------

Required:
     probability vector (PV) and sum over PV; or probability mass
     function(PMF), sum over PV and domain; or or cumulative distribution
     function (CDF)

Speed:
     Set-up: fast, Sampling: very slow (linear in expectation)

Reinit:
     supported

Reference:
     [HLD04: Sect.3.1.1; Alg.3.1]



   DSS samples from arbitrary discrete distributions.  Random numbers are
generated by the inversion method, i.e.,

  1. Generate a random number U ~ U(0,1).

  2. Find smallest integer I such that F(I) = P(X<=I) >= U.

   Step (2) is the crucial step. Using sequential search requires O(E(X))
comparisons, where E(X) is the expectation of the distribution. Thus this
method is only recommended when only a few random variates from the given
distribution are required.  Otherwise, table methods like DGT (*note DGT::) or
DAU (*note DAU::) are much faster. These methods also need not the sum over the
PMF (or PV) as input. On the other hand, however, these methods always compute
a table.

   DSS runs with the PV, the PMF, or the CDF of the distribution.  It uses
actually uses the first one in this list (in this ordering) that could be found.

How To Use
..........

It works with a discrete distribution object with contains at least the PV, the
PMF, or the CDF.

   It is possible to change the parameters and the domain of the chosen
distribution and run `unur_reinit' to reinitialize the generator object.

Function reference
------------------

 -- Function: UNUR_PAR* unur_dss_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

5.8.7 DSTD  -  Discrete STandarD distributions
----------------------------------------------

Required:
     standard distribution from UNU.RAN library (*note Standard distributions:
     Stddist.) or discrete distribution with inverse CDF.

Speed:
     Set-up: fast, Sampling: depends on distribution and generator

Reinit:
     supported


   DSTD is a wrapper for special generators for discrete univariate standard
distributions. It only works for distributions in the UNU.RAN library of
standard distributions (*note Standard distributions: Stddist.)  or for
discrete distributions where the inverse CDF is given.  If a distribution
object is provided that is build from scratch, it must provide the inverse CDF.
Then CSTD implements the inversion method. Otherwise, the `NULL' pointer is
returned.

   For some distributions more than one special generator is possible.

How To Use
..........

Create a distribution object for a standard distribution from the UNU.RAN
library (*note Standard distributions: Stddist.), or create a discrete
distribution object and set the function for the inverse CDF using
`unur_distr_discr_set_invcdf'.  For some distributions more than one special
generator (_variants_) is possible. These can be choosen by a
`unur_dstd_set_variant' call. For possible variants *Note Standard
distributions: Stddist.  However the following are common to all distributions:

`UNUR_STDGEN_DEFAULT'
     the default generator.

`UNUR_STDGEN_FAST'
     the fastest available special generator.

`UNUR_STDGEN_INVERSION'
     the inversion method (if available).

   Notice that the variant `UNUR_STDGEN_FAST' for a special generator might be
slower than one of the universal algorithms!  Additional variants may exist for
particular distributions.

   Sampling from truncated distributions (which can be constructed by changing
the default domain of a distribution by means of `unur_distr_discr_set_domain'
or unur_dstd_chg_truncated calls) is possible but requires the inversion
method. Moreover the CDF of the distribution must be implemented.

   It is possible to change the parameters and the domain of the chosen
distribution and run `unur_reinit' to reinitialize the generator object.

Function reference
------------------

 -- Function: UNUR_PAR* unur_dstd_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for new generator. It requires a distribution object
     for a discrete univariant distribution from the UNU.RAN library of
     standard distributions (*note Standard distributions: Stddist.).

     Using a truncated distribution is allowed only if the inversion method is
     available and selected by the `unur_dstd_set_variant' call immediately
     after creating the parameter object.  Use a `unur_distr_discr_set_domain'
     call to get a truncated distribution.

 -- Function: int unur_dstd_set_variant (UNUR_PAR* PARAMETERS, unsigned VARIANT)
     Set variant (special generator) for sampling from a given distribution.
     For possible variants *note Standard distributions: Stddist.

     Common variants are `UNUR_STDGEN_DEFAULT' for the default generator,
     `UNUR_STDGEN_FAST' for (one of the) fastest implemented special
     generators, and `UNUR_STDGEN_INVERSION' for the inversion method (if
     available).  If the selected variant number is not implemented, then an
     error code is returned and the variant is not changed.

 -- Function: int unur_dstd_chg_truncated (UNUR_GEN* GENERATOR, int LEFT, int
          RIGHT)
     Change left and right border of the domain of the (truncated) distribution.
     This is only possible if the inversion method is used.  Otherwise this
     call has no effect and an error code is returned.

     Notice that the given truncated domain must be a subset of the domain of
     the given distribution. The generator always uses the intersection of the
     domain of the distribution and the truncated domain given by this call.

     It is not required to run `unur_reinit' after this call has been used.

     _Important:_ If the CDF is (almost) the same for LEFT and RIGHT and
     (almost) equal to `0' or `1', then the truncated domain is not chanced and
     the call returns an error code.

     _Notice:_ If the parameters of the distribution has been changed it is
     recommended to set the truncated domain again, since the former call might
     change the domain of the distribution but not update the values for the
     boundaries of the truncated distribution.

5.9 Methods for random matrices
===============================

Overview of methods
-------------------

Methods for matrix distributions
sample with `unur_sample_matr'

MCORR: Distribution object for random correlation matrix.

5.9.1 MCORR  -  Random CORRelation matrix
-----------------------------------------

Required:
     Distribution object for random correlation matrix

Speed:
     Set-up: fast, Sampling: depends on dimension

Reinit:
     supported

Reference:
     [DLa86: Sect.6.1; p.605] [MOa84]



   MCORR generates a random correlation matrix (Pearson's correlation).  Two
methods are used:

  1. When a random correlation matrix having given eigenvalues is sought, the
     method of Marsaglia and Olkin [MOa84] is used.  In this case, the
     correlation matrix R is given as R=PDP' where D is a diagonal matrix
     containing the eigenvalues and P is a random orthonormal matrix. In higher
     dimensions, the rounding-errors introduced in the previous matrix
     multiplications could lead to a non-symmetric correlation matrix.
     Therefore the symmetric correlation matrix is computed as R=(PDP'+P'DP)/2 .

  2. A matrix H is generated where all rows are independent random vectors of
     unit length uniformly on a sphere.  Then HH' is a correlation matrix (and
     vice versa if HH' is a correlation matrix then the rows of H are random
     vectors on a sphere).


   Notice that due to round-off errors the generated matrices might not be
positive definite in extremely rare cases (especially when the given
eigenvalues are amost 0).

   There are many other possibilites (distributions) of sampling the random
rows from a sphere. The chosen methods are simple but does not result in a
uniform distriubution of the random correlation matrices.

   It only works with distribution objects of random correlation matrices
(*note Random Correlation Matrix: correlation.).

How To Use
..........

Create a distibution object for random correlation matrices by a
`unur_distr_correlation' call (*note Random Correlation Matrix: correlation.).

   When a correlation matrix with given eigenvalues should be generated, these
eigenvalues can be set by a `unur_mcorr_set_eigenvalues' call.

   Otherwise, a faster algorithm is used that generates correlation matrices
with random eigenstructure.

   Notice that due to round-off errors, there is a (small) chance that the
resulting matrix is not positive definite for a Cholesky decomposition
algorithm, especially when the dimension of the distribution is high.

   It is possible to change the given eigenvalues using
`unur_mcorr_chg_eigenvalues' and run `unur_reinit' to reinitialize the
generator object.

Function reference
------------------

 -- Function: UNUR_PAR* unur_mcorr_new (const UNUR_DISTR* DISTRIBUTION)
     Get default parameters for generator.

 -- Function: int unur_mcorr_set_eigenvalues (UNUR_PAR* PAR, const DOUBLE*
          EIGENVALUES)
     Sets the (optional) eigenvalues of the correlation matrix.  If set, then
     the Marsaglia and Olkin algorithm will be used to generate random
     correlation matrices with given eigenvalues.

     Important: the given eigenvalues of the correlation matrix must be
     strictly positive and sum to the dimension of the matrix.  If non-positive
     eigenvalues are attempted, no eigenvalues are set and an error code is
     returned.  In case, that their sum is different from the dimension, an
     implicit scaling to give the correct sum is performed.

 -- Function: int unur_mcorr_chg_eigenvalues (UNUR_GEN* GEN, const DOUBLE*
          EIGENVALUES)
     Change the eigenvalues of the correlation matrix.  One must run
     `unur_reinit' to reinitialize the generator object then.

5.10 Methods for uniform univariate distributions
=================================================

5.10.1 UNIF  -  wrapper for UNIForm random number generator
-----------------------------------------------------------

UNIF is a simple wrapper that makes it possible to use a uniform random number
generator as a UNU.RAN generator. There are no parameters for this method.

How To Use
..........

Create a generator object with `NULL' as argument. The created generator object
returns raw random numbers from the underlying uniform random number generator.

Function reference
------------------

 -- Function: UNUR_PAR* unur_unif_new (const UNUR_DISTR* DUMMY)
     Get default parameters for generator.  UNIF does not need a distribution
     object. DUMMY is not used and can (should) be set to `NULL'. It is used to
     keep the API consistent.

5.11 Meta Methods for univariate distributions
==============================================

Example
-------

     /* ------------------------------------------------------------- */
     /* File: example_mixt.c                                          */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */
     /* Mixture of a Gaussian and a Cauchy distribution.              */
     /* ------------------------------------------------------------- */

     int main(void)
     {
       /* Declare the three UNURAN objects.                           */
       UNUR_DISTR *distr;   /* distribution object                    */
       UNUR_PAR   *par;     /* parameter object                       */
       UNUR_GEN   *comp[2]; /* array of generator objects (components)*/
       UNUR_GEN   *gen;     /* generator object for mixture           */

       double prob[2];      /* array of probabilities                 */

       int    i;            /* loop variable                          */
       double x;            /* will hold the random number            */

       /* Create generators for components                            */
       distr = unur_distr_normal(NULL,0);   /* Gaussian distribution  */
       par = unur_pinv_new(distr);          /* choose method PINV     */
       comp[0] = unur_init(par);            /* initialize             */
       unur_distr_free(distr);              /* free distribution obj. */

       distr = unur_distr_cauchy(NULL,0);   /* Cauchy distribution    */
       par = unur_tdr_new(distr);           /* choose method TDR      */
       comp[1] = unur_init(par);            /* initialize             */
       unur_distr_free(distr);              /* free distribution obj. */

       /* Probabilities for components (need not sum to 1)            */
       prob[0] = 0.4;
       prob[1] = 0.3;

       /* Create mixture */
       par = unur_mixt_new(2,prob,comp);

       /* Initialize generator object                                 */
       gen = unur_init(par);

       /* we do not need the components any more */
       for (i=0; i<2; i++)
         unur_free(comp[i]);

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* Now you can use the generator object `gen' to sample from   */
       /* the distribution. Eg.:                                      */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* When you do not need the generator object any more, you     */
       /* can destroy it.                                             */
       unur_free(gen);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

Example (Inversion)
-------------------

     /* ------------------------------------------------------------- */
     /* File: example_mixt_inv.c                                      */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header file.                                   */
     #include <unuran.h>

     /* ------------------------------------------------------------- */
     /* Mixture of truncated Gaussian on (-INFINITY,0] and            */
     /* Exponential distribution on [0,INFINITY)                      */
     /* ------------------------------------------------------------- */

     int main(void)
     {
       /* Declare the three UNURAN objects.                           */
       UNUR_DISTR *distr;   /* distribution object                    */
       UNUR_PAR   *par;     /* parameter object                       */
       UNUR_GEN   *comp[2]; /* array of generator objects (components)*/
       UNUR_GEN   *gen;     /* generator object for mixture           */

       double prob[2];      /* array of probabilities                 */

       int    i;            /* loop variable                          */
       double x;            /* will hold the random number            */

       /* Create generators for components                            */
       distr = unur_distr_normal(NULL,0);   /* Gaussian distribution  */
       unur_distr_cont_set_domain(distr,-UNUR_INFINITY,0);
       par = unur_pinv_new(distr);          /* choose method PINV     */
       comp[0] = unur_init(par);            /* initialize             */
       unur_distr_free(distr);              /* free distribution obj. */

       distr = unur_distr_exponential(NULL,0); /* Exponential distr.  */
       par = unur_pinv_new(distr);          /* choose method PINV     */
       comp[1] = unur_init(par);            /* initialize             */
       unur_distr_free(distr);              /* free distribution obj. */

       /* Probabilities for components (need not sum to 1)            */
       prob[0] = 0.4;
       prob[1] = 0.3;

       /* Create mixture */
       par = unur_mixt_new(2,prob,comp);

       /* We want to use inversion for the mixture as well.           */
       /* (Thus the above order of the components is important!)      */
       unur_mixt_set_useinversion(par,TRUE);

       /* Initialize generator object                                 */
       gen = unur_init(par);

       /* we do not need the components any more */
       for (i=0; i<2; i++)
         unur_free(comp[i]);

       /* It is important to check if the creation of the generator   */
       /* object was successful. Otherwise `gen' is the NULL pointer  */
       /* and would cause a segmentation fault if used for sampling.  */
       if (gen == NULL) {
          fprintf(stderr, "ERROR: cannot create generator object\n");
          exit (EXIT_FAILURE);
       }

       /* Now you can use the generator object `gen' to sample from   */
       /* the distribution. Eg.:                                      */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* When you do not need the generator object any more, you     */
       /* can destroy it.                                             */
       unur_free(gen);

       exit (EXIT_SUCCESS);

     } /* end of main() */

     /* ------------------------------------------------------------- */

5.11.1 MIXT  -  MIXTure of distributions
----------------------------------------

MIXT allows to sample from a mixture of univariate distributions.

   Let f_1,...,f_n be PDFs of various distributions called the components and
(p_1,...,p_n) be a probability vector. Then f(x) = p_1 * f_1(x) + ...+ p_n *
f_n(x) is the PDF of the so called mixture of these distributions.

   Method MIXT takes generator objects for the components and a probability
vector and creates a generator object for this mixture.

   The sampling part works as follows:

  1. Generate an index J as the realisation of a discrete random variate with
     the given probability vector.  This is done by means of method DGT (*note
     Guide Table method: DGT.).

  2. Generate a random variate X with PDF f_J.

   When the (interior of the) domains of the the components are disjoint then
it is possible to sample from the mixture by inversion, provided that the
following conditions are met:

   - The generator objects must use an inversion method for each component.

   - The domains of the PDFs f_i must not overlap.

   - The components must be ordered with respect to their domains.

How To Use
..........

Create generator objects for the components of the mixture and store the
corresponding pointers in an array.  Store all probabilities an a double array
of the same size.  Create the parameter object for the generator of the mixture
distribution by means of `unur_mixt_new'.  The components of the mixture can be
any continuous or discrete univariate distributions. This also includes
generators for empirical distributions and mixtures of distributions.  In
particular, mixtures can also be defined recursively.

   _Remark:_ The components of the mixture can be continuous or discrete
distributions. The resulting mixture, however, is always a continuous
distribution and thus `unur_sample_cont' must be used!

   The inversion method can be switched on by means of
`unur_mixt_set_useinversion' call.  However, the conditions for this method
must then be met.  Otherwise, initialization of the mixture object fails.

Function reference
------------------

 -- Function: UNUR_PAR* unur_mixt_new (int N, const DOUBLE* PROB, UNUR_GEN**
          COMP)
     Get default parameters for the generator for a mixture of the
     distributions given in the array COMP (components) of length N. The
     probabilities are given by PROB.

     The generators in COMP must be objects for (continuous or discrete)
     univariate distributions

 -- Function: int unur_mixt_set_useinversion (UNUR_PAR* PARAMETERS, int USEINV)
     If USEINV is `TRUE', then the inversion method is used for sampling from
     the mixture distribution.

     However, the following conditions must be satisfied:

        - The generator objects must use an inversion method for each component.

        - The domains of the components must not overlap.

        - The components must be ordered with respect to their domains.

     If one of these conditions is violated, then initialization of the mixture
     object fails.

     Default is `FALSE'.

6 Using uniform random number generators
****************************************

UNU.RAN is designed to work with many sources of (pseudo-) random numbers or
low discrepancy numbers (so called quasi-random numbers) for almost all tasks
in discrete event simulation, (quasi-) Monte Carlo integration or any other
stochastic methods. Hence UNU.RAN uses pointers to access uniform (pseudo-)
random number generators (URNG).

   Each UNU.RAN (non-uniform random variate) generator object has a pointer to
a URNG object. Thus each UNU.RAN generator object may have its own
(independent) URNG or several generator objects can share the same URNG.

   If no URNG is provided for a parameter or generator object a default
generator is used which is the same for all generators. This URNG is defined in
`unuran_config.h' at compile time and can be changed at runtime.

   UNU.RAN uses a unified interface for all sources of random numbers.
Unfortunately, the API for random number generators, like the `GSL' (GNU
Scientific Library), Otmar Lendl's `prng' (Pseudo random number generators), or
a single function implemented by the user herself, are quite different.  Hence
an object of type `UNUR_URNG' is introduced to store the URNG. Thus it is
possible to handle different sources of such URNGs with the unified API. It is
inspired from similar to Pierre L'Ecuyers `RngStreams' library:

   - seed the random number generator;

   - get a uniform random number;

   - reset the URNG;

   - skip to the begining next substream;

   - sample antithetic numbers;

   - delete the URNG object.

   The routine to create a URNG depends on the chosen random number generator
(i.e. library). Nevertheless, there exist wrapper functions to simplify this
task.

   Currently the following sources of uniform random numbers are directly
supported (i.e., there exist wrapper functions).  Of course other random number
generation libraries can be used.

  1. `FVOID'

     URNGs of type `double uniform(void *state)'.  The argument STATE can be
     simply ignored in the implementation of `uniform' when a global state
     variable is used.  UNU.RAN contains some build-in URNGs of this type in
     directory `src/uniform/'.

  2. `PRNG'

     URNGs from Otmar Lendl's `prng' library. It provides a very flexible way
     to sample form arbitrary URNGs by means of an object oriented programing
     paradigma. Similarly to the UNU.RAN library independent generator objects
     can be build and used.

     This library has been developed by the pLab group at the university of
     Salzburg (Austria, EU) and implemented by Otmar Lendl.  It is available
     from `http://statmath.wu.ac.at/prng/' or from the pLab site at
     `http://random.mat.sbg.ac.at/'.

     This interface must be compiled into UNU.RAN using the configure flag
     `--with-urng-prng'.

  3. `RNGSTREAM'

     Pierre L'Ecuyer's `RngStream' library for multiple independent streams of
     pseudo-random numbers.  A GNU-style package is available from
     `http://statmath.wu.ac.at/software/RngStreams/'.

     This interface must be compiled into UNU.RAN using the configure flag
     `--with-urng-rngstream'.

  4. `GSL'

     URNG from the GNU Scientific Library (GSL).  It is available from
     `http://www.gnu.org/software/gsl/'.

     This interface must be compiled into UNU.RAN using the configure flag
     `--with-urng-gsl'.


How To Use
..........

Each UNU.RAN generator object has a pointer to a uniform (pseudo-) random
number generator (URNG). It can be set via the `unur_set_urng' call. It is also
possible to read this pointer via `unur_get_urng' or change the URNG for an
existing generator object by means of `unur_chg_urng'.  It is important to note
that these calls only copy the pointer to the URNG object into the generator
object.

   If no URNG is provided for a parameter or generator object a default URNG is
used which is the same for all generators. This URNG is defined in
`unuran_config.h' at compile time. A pointer to this default URNG can be
obtained via `unur_get_default_urng'.  Nevertheless, it is also possible to
change this default URNG by another one at runtime by means of the
`unur_set_default_urng' call. However, this only takes effect for new parameter
objects.

   Some generating methods provide the possibility of correlation induction.
For this feature a second auxiliary URNG is required.  It can be set and
changed by `unur_set_urng_aux' and `unur_chg_urng_aux' calls, respectively.
Since the auxiliary URNG is by default the same as the main URNG, the auxiliary
URNG must be set after any `unur_set_urng' or `unur_chg_urng' call! Since in
special cases mixing of two URNG might cause problems, we supply a default
auxiliary generator that can be used by a `unur_use_urng_aux_default' call
(after the main URNG has been set). This default auxiliary generator can be
changed with analogous calls as the (main) default uniform generator.

   Uniform random number generators form different sources have different
programming interfaces. Thus UNU.RAN stores all information about a particular
uniform random number generator in a structure of type `UNUR_URNG'. Before a
URNG can be used with UNU.RAN an appropriate object has to be created ba a
`unur_urng_new' call.  This call takes two arguments: the pointer to the
sampling routine of the generator and a pointer to a possible argument that
stores the state of the generator. The function must be of type `double
(*sampleunif)(void *params)', but functions without any argument also work.
Additionally one can set pointers to functions for reseting or jumping the
streams generated by the URNG by the corresponding `set' calls.

   UNU.RAN provides a unified API to all sources of random numbers.  Notice,
however, that not all functions work for all random number generators (as the
respective library has not implemented the corresponding feature).

   There are wrapper functions for some libraries of uniform random number
generators to simplify the task of creating a UNU.RAN object for URNGs.  These
functions must be compiled into UNU.RAN using the corresponding configure flags
(see description of the respective interface below).

Function reference
------------------

Set and get default uniform RNGs
--------------------------------

 -- Function: UNUR_URNG* unur_get_default_urng (void)
     Get the pointer to the default URNG. The default URNG is used by all
     generators where no URNG was set explicitly by a `unur_set_urng' call.

 -- Function: UNUR_URNG* unur_set_default_urng (UNUR_URNG* URNG_NEW)
     Change the default URNG that is used for new parameter objects.  It
     returns the pointer to the old default URNG that has been used.

 -- Function: UNUR_URNG* unur_set_default_urng_aux (UNUR_URNG* URNG_NEW)
 -- Function: UNUR_URNG* unur_get_default_urng_aux (void)
     Analogous calls for default auxiliary generator.

Set, change and get uniform RNGs in generator objects
-----------------------------------------------------

 -- Function: int unur_set_urng (UNUR_PAR* PARAMETERS, UNUR_URNG* URNG)
     Use the URNG `urng' for the new generator. This overrides the default
     URNG. It also sets the auxiliary URNG to `urng'.

     _Important_: For multivariate distributions that use marginal
     distributions this call does not work properly.  It is then better first
     to create the generator object (by a `unur_init' call) and then change the
     URNG by means of `unur_chg_urng'.

 -- Function: UNUR_URNG* unur_chg_urng (UNUR_GEN* GENERATOR, UNUR_URNG* URNG)
     Change the URNG for the given generator. It returns the pointer to the old
     URNG that has been used by the generator.  It also changes the auxiliary
     URNG to `urng' and thus it overrides the last `unur_chg_urng_aux' call.

 -- Function: UNUR_URNG* unur_get_urng (UNUR_GEN* GENERATOR)
     Get the pointer to the URNG that is used by the GENERATOR.  This is
     usefull if two generators should share the same URNG.

 -- Function: int unur_set_urng_aux (UNUR_PAR* PARAMETERS, UNUR_URNG* URNG_AUX)
     Use the auxiliary URNG `urng_aux' for the new generator.  (Default is the
     default URNG or the URNG from the last `unur_set_urng' call. Thus if the
     auxiliary generator should be different to the main URNG,
     `unur_set_urng_aux' must be called after `unur_set_urng'.  The auxiliary
     URNG is used as second stream of uniform random number for correlation
     induction.  It is not possible to set an auxiliary URNG for a method that
     does not need one. In this case an error code is returned.

 -- Function: int unur_use_urng_aux_default (UNUR_PAR* PARAMETERS)
     Use the default auxiliary URNG.  (It must be set after `unur_get_urng'.  )
     It is not possible to set an auxiliary URNG for a method that does not use
     one (i.e. the call returns an error code).

 -- Function: int unur_chgto_urng_aux_default (UNUR_GEN* GENERATOR)
     Switch to default auxiliary URNG.  (It must be set after `unur_get_urng'.
     ) It is not possible to set an auxiliary URNG for a method that does not
     use one (i.e. the call returns an error code).

 -- Function: UNUR_URNG* unur_chg_urng_aux (UNUR_GEN* GENERATOR, UNUR_URNG*
          URNG_AUX)
     Change the auxiliary URNG for the given GENERATOR. It returns the pointer
     to the old auxiliary URNG that has been used by the generator. It has to
     be called after each `unur_chg_urng' when the auxiliary URNG should be
     different from the main URNG.  It is not possible to change the auxiliary
     URNG for a method that does not use one (i.e. the call `NULL').

 -- Function: UNUR_URNG* unur_get_urng_aux (UNUR_GEN* GENERATOR)
     Get the pointer to the auxiliary URNG that is used by the GENERATOR. This
     is usefull if two generators should share the same URNG.

Handle uniform RNGs
-------------------

_Notice:_ Some of the below function calls do not work for every source of
random numbers since not every library has implemented these features.

 -- Function: double unur_urng_sample (UNUR_URNG* URNG)
     Get a uniform random number from URNG.  If the `NULL' pointer is given,
     the default uniform generator is used.

 -- Function: double unur_sample_urng (UNUR_GEN* GEN)
     Get a uniform random number from the underlying uniform random number
     generator of generator GEN.  If the `NULL' pointer is given, the default
     uniform generator is used.

 -- Function: int unur_urng_sample_array (UNUR_URNG* URNG, double* X, int DIM)
     Set array X of length DIM with uniform random numbers sampled from
     generator URNG. If URNG is the `NULL' pointer, the default uniform
     generator is used.

     _Important:_ If URNG is based on a point set generator (this is the case
     for generators of low discrepance point sets as used in quasi-Monte Carlo
     methods) it has a "natural dimension" s.  In this case either only the
     first s entries of X are filled (if s < DIM), or the first DIM coordinates
     of the generated point are filled.

     The called returns the actual number of entries filled. In case of an
     error `0' is returned.

 -- Function: int unur_urng_reset (UNUR_URNG* URNG)
     Reset URNG object.  The routine tries two ways to reset the generator (in
     this order):

       1. It uses the reset function given by an `unur_urng_set_reset' call.

       2. It uses the seed given by the last `unur_urng_seed' call (which
          requires a seeding function given by a `unur_urng_set_seed' call).

     If neither of the two methods work resetting of the generator is not
     possible and an error code is returned.

     If the `NULL' pointer is given, the default uniform generator is reset.

 -- Function: int unur_urng_sync (UNUR_URNG* URNG)
     Jump into defined state ("sync") of the generator. This is useful when
     point generators are used where the coordinates are sampled via
     `unur_urng_sample'.  Then this call can be used to jump to the first
     coordinate of the next generated point.

 -- Function: int unur_urng_seed (UNUR_URNG* URNG, unsigned LONG SEED)
     Set SEED for generator URNG.  It returns an error code if this is not
     possible for the given URNG. If the `NULL' pointer is given, the default
     uniform generator is seeded (if possible).

     _Notice_: Seeding should be done only once for a particular generator
     (except for resetting it to the initial state).  Expertise is required
     when multiple seeds are used to get independent streams. Thus we recommend
     appropriate libraries for this task, e.g. Pierre L'Ecuyer's `RngStreams'
     package. For this library only a package seed can be set and thus the
     `unur_urng_seed' call will not have any effect to generators of this type.
     Use `unur_urng_reset' or `unur_urng_rngstream_new' instead, depending
     whether one wants to reset the stream or get a new stream that is
     independent from the previous ones.

 -- Function: int unur_urng_anti (UNUR_URNG* URNG, int ANTI)
     Switch to antithetic random numbers in URNG.  It returns an error code if
     this is not possible for the given URNG.

     If the `NULL' pointer is given, the antithetic flag of the default uniform
     generator is switched (if possible).

 -- Function: int unur_urng_nextsub (UNUR_URNG* URNG)
     Jump to start of the next substream of URNG.  It returns an error code if
     this is not possible for the given URNG.

     If the `NULL' pointer is given, the default uniform generator is set to
     the start of the next substream (if possible).

 -- Function: int unur_urng_resetsub (UNUR_URNG* URNG)
     Jump to start of the current substream of URNG.  It returns an error code
     if this is not possible for the given URNG.

     If the `NULL' pointer is given, the default uniform generator is set to
     the start of the current substream (if possible).

 -- Function: int unur_gen_sync (UNUR_GEN* GENERATOR)
 -- Function: int unur_gen_seed (UNUR_GEN* GENERATOR, unsigned LONG SEED)
 -- Function: int unur_gen_anti (UNUR_GEN* GENERATOR, int ANTI)
 -- Function: int unur_gen_reset (UNUR_GEN* GENERATOR)
 -- Function: int unur_gen_nextsub (UNUR_GEN* GENERATOR)
 -- Function: int unur_gen_resetsub (UNUR_GEN* GENERATOR)
     Analogous to `unur_urng_sync', `unur_urng_seed' , `unur_urng_anti',
     `unur_urng_reset' , `unur_urng_nextsub', and `unur_urng_resetsub', but act
     on the URNG object used by the GENERATOR object.

     _Warning:_ These calls should be used with care as it influences all
     generator objects that share the same URNG object!

API to create a new URNG object
-------------------------------

_Notice:_ These functions are provided to built a UNUR_URNG object for a
particular external random number generator from scratch. For some libraries
that contain random number generators (like the GSL) there are special calls,
e.g.  `unur_urng_gsl_new', to get such an object. Then there is no need to
change the UNUR_URNG object as it already contains all available features.

   If you have a particular library for random number generators you can either
write wrapper function like those in `src/uniform/urng_gsl.c' or write an email
to the authors of UNU.RAN to write it for you.

 -- Function: UNUR_URNG* unur_urng_new (double (* SAMPLEUNIF)(void* state ),
          void* STATE)
     Get a new URNG object.  SAMPLEUNIF is a function to the uniform sampling
     routine, STATE a pointer to its arguments which usually contains the state
     variables of the generator.

     Functions SAMPLEUNIF with a different type for P or without an argument at
     all also work. A typecast might be necessary to avoid compiler warnings or
     error messages.

     For functions SAMPLEUNIF that does not have any argument should use `NULL'
     for STATE.

     _Important:_ SAMPLEUNIF must not be the `NULL' pointer.

     There are appropriate calls that simplifies the task of creating URNG
     objects for some libraries with uniform random number generators, see
     below.

 -- Function: void unur_urng_free (UNUR_URNG* URNG)
     Destroy URNG object.  It returns an error code if this is not possible.

     If the `NULL' is given, this function does nothing.

     _Warning:_ This call must be used with care. The URNG object must not be
     used by any existing generator object!  It is designed to work in
     conjunction with the wrapper functions to create URNG objects for
     generators of a particular library.  Thus an object created by an
     `unur_urng_prng_new' call can be simply destroyed by an `unur_urng_free'
     call.

 -- Function: int unur_urng_set_sample_array (UNUR_URNG* URNG, unsigned int(*
          SAMPLEARRAY)(void* state, double* X, int DIM ))
     Set function to fill array X of length DIM with random numbers generated
     by generator URNG (if available).

 -- Function: int unur_urng_set_sync (UNUR_URNG* URNG, void (* SYNC)(void*
          state ))
     Set function for jumping into a defined state ("sync").

 -- Function: int unur_urng_set_seed (UNUR_URNG* URNG, void (* SETSEED)(void*
          state, unsigned LONG SEED ))
     Set function to seed generator URNG (if available).

 -- Function: int unur_urng_set_anti (UNUR_URNG* URNG, void (* SETANTI)(void*
          state, int ANTI ))
     Set function to switch the antithetic flag of generator URNG (if
     available).

 -- Function: int unur_urng_set_reset (UNUR_URNG* URNG, void (* RESET)(void*
          state ))
     Set function for reseting the uniform random number generator URNG (if
     available).

 -- Function: int unur_urng_set_nextsub (UNUR_URNG* URNG, void (*
          NEXTSUB)(void* state ))
     Set function that allows jumping to start of the next substream of URNG
     (if available).

 -- Function: int unur_urng_set_resetsub (UNUR_URNG* URNG, void (*
          RESETSUB)(void* state ))
     Set function that allows jumping to start of the current substream of URNG
     (if available).

 -- Function: int unur_urng_set_delete (UNUR_URNG* URNG, void (*
          FPDELETE)(void* state ))
     Set function for destroying URNG (if available).

6.1 Simple interface for uniform random number generators
=========================================================

Simple interface for URNGs of type `double uniform(void *state)'.

   UNU.RAN contains some build-in URNGs of this type:
`unur_urng_MRG31k3p'
     Combined multiple recursive generator by Pierre L'Ecuyer and Renee Touzin.

`unur_urng_fish'
     Linear congruential generator by Fishman and Moore.

`unur_urng_mstd'
     Linear congruential generator "Minimal Standard" by Park and Miller.

   Notice, however, that these generators are provided as a fallback for the
case that no state-of-the-art uniform random number generators (e.g. *note
Pierre L'Ecuyer's `Rngstream' library: URNG-RNGSTREAM.) are used.

How To Use
..........

Create an URNG object using `unur_urng_fvoid_new'.  By this call a pointer to
the sampling routine and (optional) a pointer to a reset routine are copied
into the URNG object.  Other functions, like seeding the URNG, switching to
antithetic random number, or jumping to next substream, can be added to the
URNG object by the respective calls, e.g. by `unur_urng_set_seed'.  The
following routines are supported for URNG objects of this type:

   - `unur_urng_sample'

   - `unur_urng_sample_array'

   - `unur_urng_seed' [optional]

   - `unur_urng_reset' [optional]

   - `unur_urng_free'

Function reference
------------------

 -- Function: UNUR_URNG* unur_urng_fvoid_new (double (* URAND)(void* state ),
          void (* RESET)(void* state ))
     Make a URNG object for a generator that consists of a single function call
     URAND.

     If there is no RESET function use `NULL' for the second argument.

6.2 Interface to GSL uniform random number generators
=====================================================

Interface to the uniform random number generators from the GNU Scientific
Library (GSL). Documentation and source code of this library is available from
`http://www.gnu.org/software/gsl/'.

   The interface to the GSL must be compiled into UNU.RAN using the configure
flag `--with-urng-gsl'.  Notice that the GSL has to be installed before running
`./configure'.

How To Use
..........

When using this interface `unuran_urng_gsl.h' must be included in the
corresponding C file, i.e., one must add the line
     #include <unuran_urng_gsl.h>

   Moreover, one must not forget to link the executable against `libgsl'.

   The following routines are supported for URNG objects of type GSL:

   - `unur_urng_sample'

   - `unur_urng_sample_array'

   - `unur_urng_seed'

   - `unur_urng_reset'

   - `unur_urng_free'

     /* ------------------------------------------------------------- */
     /* File: example_gsl.c                                           */
     /* ------------------------------------------------------------- */
     #ifdef UNURAN_SUPPORTS_GSL
     /* ------------------------------------------------------------- */
     /* This example makes use of the GSL library for generating      */
     /* uniform random numbers.                                       */
     /* (see http://www.gnu.org/software/gsl/)                        */
     /* To compile this example you must have set                     */
     /*   ./configure --with-urng-gsl                                 */
     /* (Of course the executable has to be linked against the        */
     /* GSL library.)                                                 */
     /* ------------------------------------------------------------- */

     /* Include UNURAN header files.                                  */
     #include <unuran.h>
     #include <unuran_urng_gsl.h>

     /* ------------------------------------------------------------- */

     int main(void)
     {
       int    i;          /* loop variable                            */
       double x;          /* will hold the random number              */

       /* Declare the three UNURAN objects.                           */
       UNUR_DISTR *distr;    /* distribution object                   */
       UNUR_PAR   *par;      /* parameter object                      */
       UNUR_GEN   *gen;      /* generator object                      */

       /* Declare objects for uniform random number generators.       */
       UNUR_URNG  *urng;     /* uniform generator objects             */

       /* GNU Scientific Library only:                                */
       /* Make a object for uniform random number generator.          */
       urng = unur_urng_gsl_new(gsl_rng_mt19937);
       if (urng == NULL) exit (EXIT_FAILURE);

       /* Create a generator object using this URNG */
       distr = unur_distr_normal( NULL, 0 );
       par = unur_tdr_new(distr);
       unur_set_urng( par, urng );
       gen = unur_init(par);
       if (gen == NULL)  exit (EXIT_FAILURE);
       unur_distr_free(distr);

       /* Now you can use the generator object `gen' to sample from   */
       /* the distribution. Eg.:                                      */
       for (i=0; i<10; i++) {
         x = unur_sample_cont(gen);
         printf("%f\n",x);
       }

       /* Destroy objects */
       unur_free(gen);
       unur_urng_free(urng);

       exit (EXIT_SUCCESS);
     } /* end of main() */

     /* ------------------------------------------------------------- */
     #else
     #include <stdio.h>
     #include <stdlib.h>
     int main(void) {
       printf("You must enable the GSL to run this example!\n\n");
       exit (77);    /* exit code for automake check routines */
     }
     #endif
     /* ------------------------------------------------------------- */

Function reference
------------------

 -- Function: UNUR_URNG* unur_urng_gsl_new (const GSL_RNG_TYPE* URNGTYPE)
     Make object for URNGs from the `GSL' (GNU Scientific Library).  URNGTYPE
     is the type of the chosen generator as described in the GSL manual (see
     Section Random Number Generation). This library is available from
     `http://www.gnu.org/software/gsl/'.

 -- Function: UNUR_URNG* unur_urng_gslptr_new (gsl_rng* URNG)
     Similar to `unur_urng_gsl_new' but it uses a pointer to a generator object
     as returned by `gsl_rng_alloc(rng_type)'; see `GSL' manual for details.

     _Notice_: There is a subtle but important difference between these two
     calls. When a generator object is created by a `unur_urng_gsl_new' call,
     then resetting of the generator works.  When a generator object is created
     by a `unur_urng_gslptr_new' call, then resetting only works after a
     `unur_urng_seed(urng,myseed)' call.

6.3 Interface to GSL generators for quasi-random points
=======================================================

Interface to the generators for quasi-random points (also called low
discrepancy point sets) from the GNU Scientific Library (GSL).  Documentation
and source code of this library is available from
`http://www.gnu.org/software/gsl/'.

   The interface to the GSL must be compiled into UNU.RAN using the configure
flag `--with-urng-gsl'.  Notice that the GSL has to be installed before running
`./configure'.

How To Use
..........

When using this interface `unuran_urng_gsl.h' must be included in the
corresponding C file, i.e., one must add the line
     #include <unuran_urng_gsl.h>

   Moreover, one must not forget to link the executable against `libgsl'.

   The following routines are supported for URNG objects of this type:

   - `unur_urng_sample'

   - `unur_urng_sample_array'

   - `unur_urng_reset'

   - `unur_urng_sync'

   - `unur_urng_free'
   `unur_urng_sync' is used to jump to the first coordinate of the next point
generated by the generator.

Function reference
------------------

 -- Function: UNUR_URNG* unur_urng_gslqrng_new (const GSL_QRNG_TYPE* QRNGTYPE,
          unsigned INT DIM)
     Make object for quasi-random point generators for dimension DIM from the
     `GSL' (GNU Scientific Library).  QRNGTYPE is the type of the chosen
     generator as described in the GSL manual (see section Quasi-Random
     Sequences).  This library is available from
     `http://www.gnu.org/software/gsl/'.

6.4 Interface to Otmar Lendl's pseudo-random number generators
==============================================================

URNGs from Otmar Lendl's `prng' library. It provides a very flexible way to
sample form arbitrary URNGs by means of an object oriented programing
paradigma. Similarly to the UNU.RAN library independent generator objects can
be build and used.

   This library has been developed by the pLab group at the university of
Salzburg (Austria, EU) and implemented by Otmar Lendl.  It is available from
`http://statmath.wu.ac.at/prng/' or from the pLab site at
`http://random.mat.sbg.ac.at/'.

   The interface to the PRNG library must be compiled into UNU.RAN using the
configure flag `--with-urng-prng'.  Notice that the PRNG library has to be
installed before running `./configure'.

How To Use
..........

When using this interface `unuran_urng_prng.h' must be included in the
corresponding C file, i.e., one must add the line
     #include <unuran_urng_prng.h>

   Moreover, one must not forget to link the executable against `libprng'.

   The following routines are supported for URNG objects of type PRNG:

   - `unur_urng_sample'

   - `unur_urng_sample_array'

   - `unur_urng_seed' (availability depends on chosen PRNG generator!)

   - `unur_urng_reset'

   - `unur_urng_free'

Function reference
------------------

 -- Function: UNUR_URNG* unur_urng_prng_new (const CHAR* PRNGSTR)
     Make object for URNGs from Otmar Lendl's `prng' package.  PRNGSTR is a
     string that contains the necessary information to create a uniform random
     number generator. For the format of this string see the `prng' user manual.

     The `prng' library provides a very flexible way to sample form arbitrary
     URNGs by means of an object oriented programing paradigma. Similarly to
     the UNU.RAN library independent generator objects can be build and used.
     The library has been developed and implemented by Otmar Lendl as member of
     the pLab group at the university of Salzburg (Austria, EU).

     It is available via anonymous ftp from `http://statmath.wu.ac.at/prng/' or
     from the pLab site at `http://random.mat.sbg.ac.at/'.

 -- Function: UNUR_URNG* unur_urng_prngptr_new (struct PRNG* URNG)
     Similar to `unur_urng_prng_new' but it uses a pointer to a generator
     object as returned by `prng_new(prngstr)'; see `prng' manual for details.

6.5 Interface to L'Ecuyer's RNGSTREAM random number generators
==============================================================

URNGs from Pierre L'Ecuyer's `RngStream' library for multiple independent
streams of pseudo-random numbers.  This library provides multiple independent
streams of pseudo-random numbers which itselves can be splitted into many
substreams. It is available from
`http://www.iro.umontreal.ca/~lecuyer/myftp/streams00/c/'.  A GNU-style package
is available from `http://statmath.wu.ac.at/software/RngStreams/'.

   The interface to the RngStream library must be compiled into UNU.RAN using
the configure flag `--with-urng-rngstream'.  Notice that the RngStream library
has to be installed before running `./configure'.

How To Use
..........

When using this interface `unuran_urng_rngstream.h' must be included in the
corresponding C file, i.e., one must add the line
     #include <unuran_urng_rngstream.h>

   Moreover, one must not forget to link the executable against the `RngStream'
library (i.e., when using the GNU-style package in UNIX like environments one
has to add `-lrngstreams' when linking an executable).

   Notice that the `rngstream' library uses a package seed, that means one
should seed the uniform random number generator only once in an application
using the routine `RngStream_SetPackageSeed':

     unsigned long seed[] = {111u, 222u, 333u, 444u, 555u, 666u};
     RngStream_SetPackageSeed(seed);

   The following routines are supported for URNG objects of this type:

   - `unur_urng_sample'

   - `unur_urng_sample_array'

   - `unur_urng_reset'

   - `unur_urng_nextsub'

   - `unur_urng_resetsub'

   - `unur_urng_anti'

   - `unur_urng_free'

Function reference
------------------

 -- Function: UNUR_URNG* unur_urng_rngstream_new (const CHAR* URNGSTR)
     Make object for URNGs from Pierre L'Ecuyer's `RngStream' library. URNGSTR
     is an arbitrary string to label a stream. It need not be unique.

 -- Function: UNUR_URNG* unur_urng_rngstreamptr_new (RngStream RNGSTREAM)
     Similar to `unur_urng_rngstream_new' but it uses a pointer to a generator
     object as returned by `RngStream_CreateStream()'.

6.6 Combine point set generator with random shifts
==================================================

Generators of type RANDOMSHIFT combine a point set generator with generators to
apply random shifts as proposed in [CPa76] :

  1. Sample and store a random vector S.

  2. Run a QMC simulation where S is added to each point of the generated
     quasi-random point (mod 1).

  3. Repeat steps 1 and 2.

How To Use
..........

Create a URNG object for a point set generator and a URNG object for a
generator to create shift vectors at random.  The meta URNG object can then be
created using `unur_urng_randomshift_new'.  Notice that only pointers to the
two underlying URNG generator objects are copied into the newly created meta
generator. Thus manipulating the meta URNG also changes the underlying URNGs
and vice versa.

   The following routines are supported for URNG objects of type RANDOMSHIFT:

   - `unur_urng_sample'

   - `unur_urng_sample_array'

   - `unur_urng_reset'

   - `unur_urng_sync'

   - `unur_urng_randomshift_nextshift'

   - `unur_urng_free'
   `unur_urng_sync' is used to jump to the first coordinate of the next point
generated by the generator.  `unur_urng_randomshift_nextshift' allows to
replace the shift vector by another randomly chosen shift vector.

   _Important:_ `unur_urng_sync' is only available if it is if it is
implemented for the underlying point set generator.

   _Important:_ `unur_urng_reset' is only available if it is available for both
underlying generators.

Function reference
------------------

 -- Function: UNUR_URNG* unur_urng_randomshift_new (UNUR_URNG* QRNG, UNUR_URNG*
          SRNG, int DIM)
     Make object for URNG with randomly shifted point sets.  QRNG is a
     generated that generates point sets of dimension DIM.  SRNG is a generated
     that generates random numbers or vectors.

     _Notice:_ Only pointers to the respective objects QRNG and SRNG are copied
     into the created meta generator. Thus manipulating the meta URNG also
     changes the underlying URNGs and vice versa.

 -- Function: int unur_urng_randomshift_nextshift (UNUR_URNG* URNG)
     Get the next (randomly chosen) vector for shifting the points set, and the
     underlying point generator QRNG is reset.

7 UNU.RAN Library of standard distributions
*******************************************

Although it is not its primary target, many distributions are already
implemented in UNU.RAN.  This section presents these available distributions
and their parameters.

   The syntax to get a distribuion object for distributions `<dname>' is:

 -- -: UNUR_DISTR* unur_distr_`<dname>' (double* PARAMS, int N_PARAMS)
     PARAMS is an array of doubles of size N_PARAMS holding the parameters.

E.g. to get an object for the gamma distribution (with shape parameter) use
     unur_distr_gamma( params, 1 );

   Distributions may have default parameters with need not be given explicitely.
E.g. The gamma distribution has three parameters: the shape, scale and location
parameter. Only the (first) shape parameter is required. The others can be
omitted and are then set by default values.


     /* alpha = 5; default: beta = 1, gamma = 0 */
     double fpar[] = {5.};
     unur_distr_gamma( fpar, 1 );

     /* alpha = 5, beta = 3; default: gamma = 0 */
     double fpar[] = {5., 3.};
     unur_distr_gamma( fpar, 2 );

     /* alpha = 5, beta = 3, gamma = -2
     double fpar[] = {5., 3., -2.};
     unur_distr_gamma( fpar, 3 );


   *Important:* Naturally the computational accuracy limits the possible
parameters. There shouldn't be problems when the parameters of a distribution
are in a "reasonable" range but e.g. the normal distribution N(10^15,1) won't
yield the desired results.  (In this case it would be better generating N(0,1)
and _then_ transform the results.)
Of course computational inaccuracy is not specific to UNU.RAN and should always
be kept in mind when working with computers.

   _Important:_ The routines of the standard library are included for
non-uniform random variate generation and not to provide special functions for
statistical computations.

Remark
------

The following keywords are used in the tables:
PDF
     probability density function, with variable x.

PMF
     probability mass function, with variable k.

constant
     normalization constant for given PDF and PMF, resp.  They must be
     multiplied by constant to get the "real" PDF and PMF.

CDF
     gives information whether the CDF is implemented in UNU.RAN.

domain
     domain PDF and PMF, resp.

parameters  N_STD (N_TOTAL): list
     list of parameters for distribution, where N_STD is the number of
     parameters for the standard form of the distribution and N_TOTAL the total
     number for the (non-standard form of the) distribution. LIST is the list
     of parameters in the order as they are stored in the array of parameters.
     Optional parameter that can be omitted are enclosed in square brackets
     `[...]'.

     A detailed list of these parameters gives then the range of valid
     parameters and defaults for optional parameters that are used when these
     are omitted.

reference
     gives reference for distribution (*note Bibliography::).

special generators
     lists available special generators for the distribution.  The first number
     is the variant that to be set by `unur_cstd_set_variant' and
     `unur_dstd_set_variant' call, respectively.  If no variant is set the
     default variant `DEF' is used.  In the table the respective abbreviations
     `DEF' and `INV' are used for `UNUR_STDGEN_DEFAULT' and
     `UNUR_STDGEN_INVERSION'.  Also the references for these methods are given
     (*note Bibliography::).

     Notice that these generators might be slower than universal methods.

     If `DEF' is ommited, the first entry is the default generator.


7.1 UNU.RAN Library of continuous univariate distributions
==========================================================

7.1.1 `F'  -  F-distribution
----------------------------

PDF:
     (x^(nu_1/2-1)) / (1+nu_1/nu_2 x)^((nu_1+nu_2)/2)

constant:
     (nu_1/nu_2)^(nu_1/2) / B(nu_1/2,nu_2/2)

domain:
     0 < x < infinity

parameters 2 (2):  nu_1, nu_2
     No.     name                    default    
     `[0]'   nu_1      > 0                      (scale)
     `[1]'   nu_2      > 0                      (scale)

reference:
     [JKBc95: Ch.27; p.322]


7.1.2 `beta'  -  Beta distribution
----------------------------------

PDF:
     (x-a)^(p-1) * (b-x)^(q-1)

constant:
     1 / (B(p,q) * (b-a)^(p+q-1))

domain:
     a < x < b

parameters 2 (4):  p, q [, a, b ]
     No.     name                    default    
     `[0]'   p         > 0                      (scale)
     `[1]'   q         > 0                      (scale)
     `[2]'   a                       0          (location, scale)
     `[3]'   b         > a           1          (location, scale)

reference:
     [JKBc95: Ch.25; p.210]


7.1.3 `cauchy'  -  Cauchy distribution
--------------------------------------

PDF:
     1/(1 + ((x-theta)/lambda)^2)

constant:
     1/(pi * lambda)

domain:
     -infinity < x < infinity

parameters 0 (2):  [ theta [, lambda ] ]
     No.     name                    default    
     `[0]'   theta                   0          (location)
     `[1]'   lambda    > 0           1          (scale)

reference:
     [JKBb94: Ch.16; p.299]

special generators:

    `INV'
          Inversion method


7.1.4 `chi'  -  Chi distribution
--------------------------------

PDF:
     x^(nu-1) * exp( -x^2/2 )

constant:
     1 / (2^((nu/2)-1) * Gamma(nu/2))

domain:
     0 <= x < infinity

parameters 1 (1):  nu
     No.     name                    default    
     `[0]'   nu        > 0                      (shape)

reference:
     [JKBb94: Ch.18; p.417]

special generators:

    `DEF'
          Ratio of Uniforms with shift (only for  nu >= 1)  [MJa87]


7.1.5 `chisquare'  -  Chisquare distribution
--------------------------------------------

PDF:
     x^((nu/2)-1) * exp( -x/2 )

constant:
     1 / (2^(nu/2) * Gamma(nu/2))

domain:
     0 <= x < infinity

parameters 1 (1):  nu
     No.     name                    default    
     `[0]'   nu        > 0                      (shape (degrees of
                                                freedom))

reference:
     [JKBb94: Ch.18; p.416]


7.1.6 `exponential'  -  Exponential distribution
------------------------------------------------

PDF:
     exp( -(x-theta)/sigma)

constant:
     1/sigma

domain:
     theta <= x < infinity

parameters 0 (2):  [ sigma [, theta ] ]
     No.     name                    default    
     `[0]'   sigma     > 0           1          (scale)
     `[1]'   theta                   0          (location)

reference:
     [JKBb94: Ch.19; p.494]

special generators:

    `INV'
          Inversion method


7.1.7 `extremeI'  -  Extreme value type I (Gumbel-type) distribution
--------------------------------------------------------------------

PDF:
     exp( -exp( -(x-zeta)/theta ) - (x-zeta)/theta )

constant:
     1/theta

domain:
     -infinity < x <infinity

parameters 0 (2):  [ zeta [, theta ] ]
     No.     name                    default    
     `[0]'   zeta                    0          (location)
     `[1]'   theta     > 0           1          (scale)

reference:
     [JKBc95: Ch.22; p.2]

special generators:

    `INV'
          Inversion method


7.1.8 `extremeII'  -  Extreme value type II (Frechet-type) distribution
-----------------------------------------------------------------------

PDF:
     exp( -((x-zeta)/theta)^(-k)) * ((x-zeta)/theta)^(-k-1)

constant:
     k/theta

domain:
     zeta < x <infinity

parameters 1 (3):  k [, zeta [, theta ] ]
     No.     name                    default    
     `[0]'   k         > 0                      (shape)
     `[1]'   zeta                    0          (location)
     `[2]'   theta     > 0           1          (scale)

reference:
     [JKBc95: Ch.22; p.2]

special generators:

    `INV'
          Inversion method


7.1.9 `gamma'  -  Gamma distribution
------------------------------------

PDF:
     ((x-gamma)/beta)^(alpha-1) * exp( -(x-gamma)/beta )

constant:
     1 / (beta * Gamma(alpha))

domain:
     gamma < x < infinity

parameters 1 (3):  alpha [, beta [, gamma ] ]
     No.     name                    default    
     `[0]'   alpha     > 0                      (shape)
     `[1]'   beta      > 0           1          (scale)
     `[2]'   gamma                   0          (location)

reference:
     [JKBb94: Ch.17; p.337]

special generators:

    `DEF'
          Acceptance Rejection combined with Acceptance Complement  [ADa74]
          [ADa82]

    `2'
          Rejection from log-logistic envelopes  [CHa77]


7.1.10 `gig'  -  Generalized Inverse Gaussian distribution
----------------------------------------------------------

PDF:
     x^(theta-1) * exp( -1/2 * omega * (x/eta + eta/x))

constant:
     not implemented!

CDF:
     not implemented!

domain:
     0 < x <infinity

parameters 2 (3):  theta, omega [, eta ]
     No.     name                    default    
     `[0]'   theta                              (shape)
     `[1]'   omega     > 0                      (scale)
     `[2]'   eta       > 0           1          (shape)

reference:
     [JKBb94: Ch.15; p.84]

special generators:

    `DEF'
          Ratio-of-Uniforms method  [Dag89]


7.1.11 `gig2'  -  Generalized Inverse Gaussian distribution
-----------------------------------------------------------

PDF:
     x^(theta-1) * exp( -1/2 * (chi/x + psi*x))

constant:
     not implemented!

CDF:
     not implemented!

domain:
     0 < x <infinity

parameters 3 (3):  theta, psi, chi
     No.     name                    default    
     `[0]'   theta                              (shape)
     `[1]'   psi       > 0                      (shape)
     `[2]'   chi       > 0                      (shape)

reference:
     [JKBb94: Ch.15; p.84]


7.1.12 `hyperbolic'  -  Hyperbolic distribution
-----------------------------------------------

PDF:
     exp( -alpha * sqrt(delta^2 + (x - mu)^2) + beta*(x-mu) )

constant:
     not implemented!

CDF:
     not implemented!

domain:
     -infinity < x <infinity

parameters 4 (4):  alpha, beta, delta, mu
     No.     name                    default    
     `[0]'   alpha     >|beta|                  (shape (tail))
     `[1]'   beta                               (shape (asymmetry))
     `[2]'   delta     > 0                      (scale)
     `[3]'   mu                                 (location)

7.1.13 `ig'  -  Inverse Gaussian distribution
---------------------------------------------

PDF:
     sqrt( lambda/2*pi*x^3 ) * exp( -(lambda*(x-mu)^2)/2*mu^2*x )

constant:
     1

CDF:
     not implemented!

domain:
     0 < x <infinity

parameters 2 (2):  mu, lambda
     No.     name                    default    
     `[0]'   mu        > 0                      (mean)
     `[1]'   lambda    > 0                      (shape)

reference:
     [JKBb94: Ch.15; p.259]


7.1.14 `laplace'  -  Laplace distribution
-----------------------------------------

PDF:
     exp( -|x-theta| / phi )

constant:
     1/(2 * phi)

domain:
     -infinity < x <infinity

parameters 0 (2):  [ theta [, phi ] ]
     No.     name                    default    
     `[0]'   theta                   0          (location)
     `[1]'   phi       > 0           1          (scale)

reference:
     [JKBc95: Ch.24; p.164]

special generators:

    `INV'
          Inversion method


7.1.15 `logistic'  -  Logistic distribution
-------------------------------------------

PDF:
     exp(-(x-alpha)/beta) * (1 + exp(-(x-alpha)/beta))^(-2)

constant:
     1/beta

domain:
     -infinity < x <infinity

parameters 0 (2):  [ alpha [, beta ] ]
     No.     name                    default    
     `[0]'   alpha                   0          (location)
     `[1]'   beta      > 0           1          (scale)

reference:
     [JKBc95: Ch.23; p.115]

special generators:

    `INV'
          Inversion method


7.1.16 `lognormal'  -  Log-Normal distribution
----------------------------------------------

PDF:
     1/(x-theta) * exp( -(log(x-theta)-zeta)^2/(2 sigma^2) )

constant:
     1/(sigma * sqrt(2 pi))

domain:
     theta <= x < infinity

parameters 2 (3):  zeta, sigma [, theta ]
     No.     name                    default    
     `[0]'   zeta                               (shape)
     `[1]'   sigma     > 0                      (shape)
     `[2]'   theta                   0          (location)

reference:
     [JKBb94: Ch.14; p. 208]


7.1.17 `lomax'  -  Lomax distribution (Pareto distribution of second kind)
--------------------------------------------------------------------------

PDF:
     (x+C)^(-(a+1))

constant:
     a * C^a

domain:
     0 <= x < infinity

parameters 1 (2):  a [, C ]
     No.     name                    default    
     `[0]'   a         > 0                      (shape)
     `[1]'   C         > 0           1          (scale)

reference:
     [JKBb94: Ch.20; p.575]

special generators:

    `INV'
          Inversion method


7.1.18 `normal'  -  Normal distribution
---------------------------------------

PDF:
     exp( -1/2 * ((x-mu)/sigma)^2 )

constant:
     1 / (sigma * sqrt(2 pi))

domain:
     -infinity < x < infinity

parameters 0 (2):  [ mu [, sigma ] ]
     No.     name                    default    
     `[0]'   mu                      0          (location)
     `[1]'   sigma     > 0           1          (scale)

reference:
     [JKBb94: Ch.13; p.80]

special generators:

    `DEF'
          ACR method (Acceptance-Complement Ratio)  [HDa90]

    `1'
          Box-Muller method  [BMa58]

    `2'
          Polar method with rejection  [MGa62]

    `3'
          Kindermann-Ramage method  [KRa76]

    `INV'
          Inversion method (slow)


7.1.19 `pareto'  -  Pareto distribution (of first kind)
-------------------------------------------------------

PDF:
     x^(-(a+1))

constant:
     a * k^a

domain:
     k < x < infinity

parameters 2 (2):  k, a
     No.     name                    default    
     `[0]'   k         > 0                      (shape, location)
     `[1]'   a         > 0                      (shape)

reference:
     [JKBb94: Ch.20; p.574]

special generators:

    `INV'
          Inversion method


7.1.20 `powerexponential'  -  Powerexponential (Subbotin) distribution
----------------------------------------------------------------------

PDF:
     exp( -|x|^tau )

constant:
     1 / (2 * Gamma(1+1/tau))

domain:
     -infinity < x < infinity

parameters 1 (1):  tau
     No.     name                    default    
     `[0]'   tau       > 0                      (shape)

reference:
     [JKBc95: Ch.24; p.195]

special generators:

    `DEF'
          Transformed density rejection (only for  tau >= 1)  [DLa86]


7.1.21 `rayleigh'  -  Rayleigh distribution
-------------------------------------------

PDF:
     x * exp( -1/2 * (x/sigma)^2 )

constant:
     1 / sigma^2

domain:
     0 <= x < infinity

parameters 1 (1):  sigma
     No.     name                    default    
     `[0]'   sigma     > 0                      (scale)

reference:
     [JKBb94: Ch.18; p.456]


7.1.22 `slash'  -  Slash distribution
-------------------------------------

PDF:
     (1 - exp(-x^2/2)) / x^2

constant:
     1 / sqrt(2 pi)

CDF:
     not implemented!

domain:
     -infinity < x < infinity

reference:
     [JKBb94: Ch.12; p.63]

special generators:

    `DEF'
          Ratio of normal and uniform random variates


7.1.23 `student'  -  Student's t distribution
---------------------------------------------

PDF:
     (1+t^2/nu)^(-(nu+1)/2)

constant:
     1 / (sqrt(nu) * B(1/2,nu/2))

CDF:
     not implemented!

domain:
     -infinity < x < infinity

parameters 1 (1):  nu
     No.     name                    default    
     `[0]'   nu        > 0                      (shape)

reference:
     [JKBc95: Ch.28; p.362]


7.1.24 `triangular'  -  Triangular distribution
-----------------------------------------------

PDF:
     2*x / H,           for  0 <= x <= H

     2*(1-x) / (1-H),   for  H <= x <= 1

constant:
     1

domain:
     0 <= x <= 1

parameters 0 (1):  [ H ]
     No.     name                    default    
     `[0]'   H         0 <= H <= 1   1/2        (shape)

reference:
     [JKBc95: Ch.26; p.297]

special generators:

    `INV'
          Inversion method


7.1.25 `uniform'  -  Uniform distribution
-----------------------------------------

PDF:
     1 / (b-a)

constant:
     1

domain:
     a < x < b

parameters 0 (2):  [ a, b ]
     No.     name                    default    
     `[0]'   a                       0          (location)
     `[1]'   b         > a           1          (location)

reference:
     [JKBc95: Ch.26; p.276]

special generators:

    `INV'
          Inversion method


7.1.26 `weibull'  -  Weibull distribution
-----------------------------------------

PDF:
     ((x-zeta)/alpha)^(c-1) * exp( -((x-zeta)/alpha)^c )

constant:
     c / alpha

domain:
     zeta < x < infinity

parameters 1 (3):  c [, alpha [, zeta ] ]
     No.     name                    default    
     `[0]'   c         > 0                      (shape)
     `[1]'   alpha     > 0           1          (scale)
     `[2]'   zeta                    0          (location)

reference:
     [JKBb94: Ch.21; p.628]

special generators:

    `INV'
          Inversion method


7.2 UNU.RAN Library of continuous multivariate distributions
============================================================

7.2.1 `copula'  -  Copula (distribution with uniform marginals)
---------------------------------------------------------------


   `UNUR_DISTR *unur_distr_copula(int dim, const double *rankcorr)' creates a
distribution object for a copula with DIM components.  RANKCORR is an array of
size DIMxDIM and holds the rank correlation matrix (Spearman's correlation),
where the rows of the matrix are stored consecutively in this array. The `NULL'
pointer can be used instead the identity matrix.

   If COVAR is not a valid rank correlation matrix (i.e., not positive
definite) then no distribution object is created and `NULL' is returned.

7.2.2 `multicauchy'  -  Multicauchy distribution
------------------------------------------------

PDF:
     f(x) = 1 / ( 1 + (x-mu)^t . Sigma^(-1) . (x-mu) )^((dim+1)/2)

constant:
     Gamma((dim+1)/2) / ( pi^((dim+1)/2) * sqrt(det(Sigma)) )

domain:
     -infinity^(dim) < x < infinity^(dim)

parameters 0 (2):  [ mu, Sigma ]
     No.     name                    default    
     `[0]'   mu                      (0,...,0)  (location)
     `[1]'   Sigma     Symm, Pos.    I          (shape)
                       def.                     

special generators:

    `DEF'
          Cholesky factor


7.2.3 `multiexponential'  -  Multiexponential distribution
----------------------------------------------------------

PDF:
     f(x) = Prod_(i=0)^(i=dim-1) exp(-(dim-i) (x_(i)-x_(i-1) -
     (theta_i-theta_(i-1)) ) / sigma_i); with x_(-1)=0 and theta_(i-1)=0

constant:
     Prod_(i=0)^(i=dim-1) 1/sigma_i

domain:
     0^(dim) <= x < infinity^(dim)

parameters 0 (2):  [ sigma, theta ]
     No.     name                    default    
     `[0]'   sigma                   (1,...,1)  (shape)
     `[1]'   theta                   (0,...,0)  (location)

7.2.4 `multinormal'  -  Multinormal distribution
------------------------------------------------

PDF:
     f(x) = exp( -1/2 * (x-mu)^t . Sigma^(-1) . (x-mu) )

constant:
     1 / ( (2 pi)^(dim/2) * sqrt(det(Sigma)) )

domain:
     -infinity^(dim) < x < infinity^(dim)

parameters 0 (2):  [ mu, Sigma ]
     No.     name                    default    
     `[0]'   mu                      (0,...,0)  (location)
     `[1]'   Sigma     Symm, Pos.    I          (shape)
                       def.                     

reference:
     [KBJe00: Ch.45; p.105]


7.2.5 `multistudent'  -  Multistudent distribution
--------------------------------------------------

PDF:
     f(x) = 1 / ( 1 + (x-mu)^t . Sigma^(-1) . (x-mu) / m)^((dim+m)/2) )

constant:
     Gamma((dim+m)/2) / ( Gamma(m/2) (m*pi)^(dim/2) * sqrt(det(Sigma)) )

domain:
     -infinity^(dim) < x < infinity^(dim)

parameters 0 (3):  [ m, mu, Sigma ]
     No.     name                    default    
     `[0]'   m         m>0           1          (location)
     `[1]'   mu                      (0,...,0)  (location)
     `[2]'   Sigma     Symm, Pos.    I          (shape)
                       def.                     

7.3 UNU.RAN Library of discrete univariate distributions
========================================================

At the moment there are no CDFs implemented for discrete distribution.  Thus
`unur_distr_discr_upd_pmfsum' does not work properly for truncated distribution.

7.3.1 `binomial'  -  Binomial distribution
------------------------------------------

PMF:
     (n \choose k) * p^k * (1-p)^(n-k)

constant:
     1

domain:
     0 <= k <= n

parameters 2 (2):  n, p
     No.     name                    default    
     `[0]'   n         >= 1                     (no. of elements)
     `[1]'   p         0 < p < 1                (shape)

reference:
     [JKKa92: Ch.3; p.105]

special generators:

    `DEF'
          Ratio of Uniforms/Inversion  [STa89]


7.3.2 `geometric'  -  Geometric distribution
--------------------------------------------

PMF:
     p * (1-p)^k

constant:
     1

domain:
     0 <= k < infinity

parameters 1 (1):  p
     No.     name                    default    
     `[0]'   p         0 < p < 1                (shape)

reference:
     [JKKa92: Ch.5.2; p.201]

special generators:

    `INV'
          Inversion method


7.3.3 `hypergeometric'  -  Hypergeometric distribution
------------------------------------------------------

PMF:
     (M \choose k) * (N-M \choose n-k) / (N \choose n)

constant:
     1

domain:
     max(0,n-N+M) <= k <= min(n,M)

parameters 3 (3):  N, M, n
     No.     name                    default    
     `[0]'   N         >= 1                     (no. of elements)
     `[1]'   M         1 <= M <= N              (shape)
     `[2]'   n         1 <= n <= N              (shape)

reference:
     [JKKa92: Ch.6; p.237]

special generators:

    `DEF'
          Ratio of Uniforms/Inversion  [STa89]


7.3.4 `logarithmic'  -  Logarithmic distribution
------------------------------------------------

PMF:
     theta^k / k

constant:
     - log( 1.-theta);

domain:
     1 <= k < infinity

parameters 1 (1):  theta
     No.     name                    default    
     `[0]'   theta     0 < theta <              (shape)
                       1                        

reference:
     [JKKa92: Ch.7; p.285]

special generators:

    `DEF'
          Inversion/Transformation  [KAa81]


7.3.5 `negativebinomial'  -  Negative Binomial distribution
-----------------------------------------------------------

PMF:
     (k+r-1 \choose r-1) * p^r * (1-p)^k

constant:
     1

domain:
     0 <= k < infinity

parameters 2 (2):  p, r
     No.     name                    default    
     `[0]'   p         0 < p < 1                (shape)
     `[1]'   r         > 0                      (shape)

reference:
     [JKKa92: Ch.5.1; p.200]


7.3.6 `poisson'  -  Poisson distribution
----------------------------------------

PMF:
     theta^k / k!

constant:
     exp(theta)

domain:
     0 <= k < infinity

parameters 1 (1):  theta
     No.     name                    default    
     `[0]'   theta     > 0                      (shape)

reference:
     [JKKa92: Ch.4; p.151]

special generators:

    `DEF'
          Tabulated Inversion combined with Acceptance Complement  [ADb82]

    `2'
          Tabulated Inversion combined with Patchwork Rejection  [ZHa94]


7.4 UNU.RAN Library of random matrices
======================================

7.4.1 `correlation'  -  Random correlation matrix
-------------------------------------------------


   `UNUR_DISTR *unur_distr_correlation( int n )' creates a distribution object
for a random correlation matrix of N rows and columns. It can be used with
method MCORR (*note Random Correlation Matrix: MCORR.) to generate random
correlation matrices of the given size.

8 Error handling and Debugging
******************************

UNU.RAN routines report an error whenever they cannot perform the requested
task. Additionally it is possible to get information about the generated
distribution of generator objects for debugging purposes. However, the latter
must be enabled when compiling and installing the library. (It is disabled by
default.)  This chapter describes all necessary details:

   * Choose an output stream to for writing the requested information.

   * Select a debugging level.

   * Select an error handler.

   * Write your own error handler.

   * Get more information for a particular error code.

8.1 Output streams
==================

UNU.RAN uses a logfile for writing all error messages, warnings, and debugging
information onto an output stream. This stream can be set at runtime by the
`unur_set_stream' call.  If no such stream is given by the user a default
stream is used by the library: all messages are written into the file
`unuran.log' in the current working directory. The name of this logfile is
defined by the macro `UNUR_LOG_FILE' in `unuran_config.h'.  (If UNU.RAN fails
to open this file for writing, `stderr' is used instead.)

   To destinguish between messages for different objects each of these has its
own identifier which is composed by the name of the distribution obejct and
generator type, resp., followed by a dot and three digits.  (If there are more
than 999 generators then the identifiers are not unique.)

   _Remark:_ Writting debugging information must be switched on at compile time
using the configure flag `--enable-logging', see *note Debugging: Debug.

Function reference
------------------

 -- Function: FILE* unur_set_stream (FILE* NEW_STREAM)
     This function sets a new file handler for the output stream, NEW_STREAM,
     for the UNU.RAN library routines. The previous handler is returned (so
     that you can restore it later).  Note that the pointer to a user defined
     file handler is stored in a static variable, so there can be only one
     output stream handler per program. This function should be not be used in
     multi-threaded programs except to set up a program-wide error handler from
     a master thread.

     The `NULL' pointer is not allowed.  (If you want to disable logging of
     debugging information use unur_set_default_debug(UNUR_DEBUG_OFF) instead.
     If you want to disable error messages at all use
     `unur_set_error_handler_off'.  )

 -- Function: FILE* unur_get_stream (void)
     Get the file handle for the current output stream. It can be used to allow
     applications to write additional information into the logfile.

8.2 Debugging
=============

The UNU.RAN library has several debugging levels which can be switched on/off
by debugging flags. This debugging feature must be enabled when building the
library using the `--enable-logging' configure flag.

   The debugging levels range from print a short description of the created
generator object to a detailed description of hat functions and tracing the
sampling routines. The output is printed onto the debugging output stream
(*note Output streams: Output_streams.).

   The debugging flags can be set or changed by the respective calls
`unur_set_debug' and `unur_chg_debug' independently for each generator.

   By default flag `UNUR_DEBUG_INIT' (see below) is used. This default flags is
set by the macro `UNUR_DEBUGFLAG_DEFAULT' in `unuran_config.h' and can be
changed at runtime by a `unur_set_default_debug' call.

   Off course these debugging flags depend on the chosen method. Since most of
these are merely for debugging the library itself, a description of the flags
are given in the corresponding source files of the method.  Nevertheless, the
following flags can be used with all methods.

   Common debug flags:

`UNUR_DEBUG_OFF'
     switch off all debuging information

`UNUR_DEBUG_ALL'
     all avaivable information

`UNUR_DEBUG_INIT'
     parameters of generator object after initialization

`UNUR_DEBUG_SETUP'
     data created at setup

`UNUR_DEBUG_ADAPT'
     data created during adaptive steps

`UNUR_DEBUG_SAMPLE'
     trace sampling

   Notice that these are flags which could be combined using the `|' operator.

   Almost all routines check a given pointer before they read from or write to
the given address. This does not hold for time-critical routines like all
sampling routines. Thus you are responsible for checking a pointer that is
returned from a `unur_init' call.  However, it is possible to turn on checking
for invalid `NULL' pointers even in such time-critical routines by building the
library using the `--enable-check-struct' configure flag.

   Another debugging tool used in the library are magic cookies that validate a
given pointer. It produces an error whenever a given pointer points to an
object that is invalid in the context.  The usage of magic cookies is also
switched on by the `--enable-check-struct' configure flag.

Function reference
------------------

 -- Function: int unur_set_debug (UNUR_PAR* PARAMETERS, unsigned DEBUG)
     Set debugging flags for generator.

 -- Function: int unur_chg_debug (UNUR_GEN* GENERATOR, unsigned DEBUG)
     Change debugging flags for generator.

 -- Function: int unur_set_default_debug (unsigned DEBUG)
     Change default debugging flag.

8.3 Error reporting
===================

UNU.RAN routines report an error whenever they cannot perform the requested
task. For example, applying transformed density rejection to a distribution
that violates the T-concavity condition, or trying to set a parameter that is
out of range, result in an error message.  It might also happen that the setup
fails for transformed density rejection for a T-concave distribution with some
extreme density function simply because of round-off errors that makes the
generation of a hat function numerically impossible.  Situations like this may
happen when using black box algorithms and you should check the return values
of all routines.

   All `..._set_...', and `..._chg_...' calls return `UNUR_SUCCESS' if they
could be executed successfully. Otherwise, some error codes are returned if it
was not possible to set or change the desired parameters, e.g. because the
given values are out of range, or simply because the set call does not work for
the chosen method.

   All routines that return a pointer to the requested object will return a
`NULL' pointer in case of error.  (Thus you should always check the pointer to
avoid possible segmentation faults. Sampling routines usually do not check the
given pointer to the generator object.)

   The library distinguishes between two major classes of error:

_(fatal) errors:_
     The library was not able to construct the requested object.

_warnings:_
     Some problems encounters while constructing a generator object. The
     routine has tried to solve the problem but the resulting object might not
     be what you want. For example, chosing a special variant of a method does
     not work and the initialization routine might switch to another variant.
     Then the generator produces random variates of the requested distribution
     but correlation induction is not possible. However, it also might happen
     that changing the domain of a distribution has failed. Then the generator
     produced random variates with too large/too small range, i.e. their
     distribution is not correct.

   It is obvious from the example that this distinction between errors and
warning is rather crude and sometimes arbitrary.

   UNU.RAN routines use the global variable UNUR_ERRNO to report errors,
completely analogously to ERRNO in the ANSI C standard library.  (However this
approach is not thread-safe. There can be only one instance of a global
variable per program. Different threads of execution may overwrite UNUR_ERRNO
simultaneously).  Thus when an error occurs the caller of the routine can
examine the error code in UNUR_ERRNO to get more details about the reason why a
routine failed. You get a short description of the error by a
`unur_get_strerror' call.  All the error code numbers have prefix `UNUR_ERR_'
and expand to non-zero constant unsigned integer values.  Error codes are
divided into six main groups, see *note Error codes: Errno.

   Alternatively, the variable UNUR_ERRNO can also read by a `unur_get_errno'
call and can be reset by the `unur_reset_errno' call (this is in particular
required for the Windows version of the library).

   Additionally, there exists a error handler (*note Error handlers:
Error_handlers.) that is invoked in case of an error.

   In addition to reporting errors by setting error codes in UNUR_ERRNO, the
library also has an error handler function. This function is called by other
library functions when they report an error, just before they return to the
caller (*note Error handlers: Error_handlers.).  The default behavior of the
error handler is to print a short message:

     AROU.004: [error] arou.c:1500 - (generator) condition for method violated:
     AROU.004: ..>  PDF not unimodal

   The purpose of the error handler is to provide a function where a breakpoint
can be set that will catch library errors when running under the debugger. It
is not intended for use in production programs, which should handle any errors
using the return codes.

Function reference
------------------

 -- Variable: extern int unur_errno
     Global variable for reporting diagnostics of error.

 -- Function: int unur_get_errno (void)
     Get current value of global variable UNUR_ERRNO.

 -- Function: void unur_reset_errno (void)
     Reset global variable UNUR_ERRNO to `UNUR_SUCCESS' (i.e., no errors
     occured).

 -- Function: const char* unur_get_strerror (const INT ERRNOCODE)
     Get a short description for error code value.

8.4 Error codes
===============

List of error codes
...................

   * Procedure executed successfully (no error)
    `UNUR_SUCCESS (0x0u)'
          success (no error)

   * Errors that occurred while handling distribution objects.
    `UNUR_ERR_DISTR_SET'
          set failed (invalid parameter).

    `UNUR_ERR_DISTR_GET'
          get failed (parameter not set).

    `UNUR_ERR_DISTR_NPARAMS'
          invalid number of parameters.

    `UNUR_ERR_DISTR_DOMAIN'
          parameter(s) out of domain.

    `UNUR_ERR_DISTR_GEN'
          invalid variant for special generator.

    `UNUR_ERR_DISTR_REQUIRED'
          incomplete distribution object, entry missing.

    `UNUR_ERR_DISTR_UNKNOWN'
          unknown distribution, cannot handle.

    `UNUR_ERR_DISTR_INVALID'
          invalid distribution object.

    `UNUR_ERR_DISTR_DATA'
          data are missing.

    `UNUR_ERR_DISTR_PROP'
          desired property does not exist

   * Errors that occurred while handling parameter objects.
    `UNUR_ERR_PAR_SET'
          set failed (invalid parameter)

    `UNUR_ERR_PAR_VARIANT'
          invalid variant -> using default

    `UNUR_ERR_PAR_INVALID'
          invalid parameter object

   * Errors that occurred while handling generator objects.
    `UNUR_ERR_GEN'
          error with generator object.

    `UNUR_ERR_GEN_DATA'
          (possibly) invalid data.

    `UNUR_ERR_GEN_CONDITION'
          condition for method violated.

    `UNUR_ERR_GEN_INVALID'
          invalid generator object.

    `UNUR_ERR_GEN_SAMPLING'
          sampling error.

    `UNUR_ERR_NO_REINIT'
          reinit routine not implemented.

    `UNUR_ERR_NO_QUANTILE'
          quantile routine not implemented.

   * Errors that occurred while handling URNG objects.
    `UNUR_ERR_URNG'
          generic error with URNG object.

    `UNUR_ERR_URNG_MISS'
          missing functionality.

   * Errors that occurred while parsing strings.
    `UNUR_ERR_STR'
          error in string.

    `UNUR_ERR_STR_UNKNOWN'
          unknown keyword.

    `UNUR_ERR_STR_SYNTAX'
          syntax error.

    `UNUR_ERR_STR_INVALID'
          invalid parameter.

    `UNUR_ERR_FSTR_SYNTAX'
          syntax error in function string.

    `UNUR_ERR_FSTR_DERIV'
          cannot derivate function.

   * Other run time errors.
    `UNUR_ERR_DOMAIN'
          argument out of domain.

    `UNUR_ERR_ROUNDOFF'
          (serious) round-off error.

    `UNUR_ERR_MALLOC'
          virtual memory exhausted.

    `UNUR_ERR_NULL'
          invalid `NULL' pointer.

    `UNUR_ERR_COOKIE'
          invalid cookie.

    `UNUR_ERR_GENERIC'
          generic error.

    `UNUR_ERR_SILENT'
          silent error (no error message).

    `UNUR_ERR_INF'
          infinity occured.

    `UNUR_ERR_NAN'
          NaN occured.

    `UNUR_ERR_COMPILE'
          Requested routine requires different compilation switches.
          Recompilation of library necessary.

    `UNUR_ERR_SHOULD_NOT_HAPPEN'
          Internal error, that should not happen.  Please report this bug!


8.5 Error handlers
==================

The default behavior of the UNU.RAN error handler is to print a short message
onto the output stream, usually a logfile (*note Output streams:
Output_streams.), e.g.,

     AROU.004: [error] arou.c:1500 - (generator) condition for method violated:
     AROU.004: ..>  PDF not unimodal

   This error handler can be switched off using the `unur_set_error_handler_off'
call, or replace it by a new one.  Thus it allows to set a breakpoint that will
catch library errors when running under the debugger. It also can be used to
redirect error messages when UNU.RAN is included in general purpose libraries
or in interactive programming environments.

 -- Data Type: UNUR_ERROR_HANDLER
     This is the type of UNU.RAN error handler functions. An error handler will
     be passed six arguments which specify the identifier of the object where
     the error occured (a string), the name of the source file in which it
     occurred (also a string), the line number in that file (an integer), the
     type of error (a string: `"error"' or `"warning"'), the error number (an
     integert), and the reason for the error (a string).  The source file and
     line number are set at compile time using the `__FILE__' and `__LINE__'
     directives in the preprocessor.  The error number can be translated into a
     short description using a `unur_get_strerror' call.  An error handler
     function returns type `void'.

     Error handler functions should be defined like this,
          void my_handler(
                     const char *objid,
                     const char *file,
                     int line,
                     const char *errortype,
                     int unur_errno,
                     const char *reason )

   To request the use of your own error handler you need the call
`unur_set_error_handler'.

Function reference
------------------

 -- Function: UNUR_ERROR_HANDLER* unur_set_error_handler (UNUR_ERROR_HANDLER*
          NEW_HANDLER)
     This function sets a new error handler, NEW_HANDLER, for the UNU.RAN
     library routines. The previous handler is returned (so that you can
     restore it later). Note that the pointer to a user defined error handler
     function is stored in a static variable, so there can be only one error
     handler per program. This function should be not be used in multi-threaded
     programs except to set up a program-wide error handler from a master
     thread.

     To use the default behavior set the error handler to `NULL'.

 -- Function: UNUR_ERROR_HANDLER* unur_set_error_handler_off (void)
     This function turns off the error handler by defining an error handler
     which does nothing (except of setting UNUR_ERRNO.  The previous handler is
     returned (so that you can restore it later).

9 Testing
*********

The following routines can be used to test the performance of the implemented
generators and can be used to verify the implementions.  They are declared in
`unuran_tests.h' which has to be included.

Function reference
------------------

 -- Function: void unur_run_tests (UNUR_PAR* PARAMETERS, unsigned TESTS, FILE*
          OUT)
     Run a battery of tests.  The following tests are available (use `|' to
     combine these tests):
    `UNUR_TEST_ALL'
          run all possible tests.

    `UNUR_TEST_TIME'
          estimate generation times.

    `UNUR_TEST_N_URNG'
          count number of uniform random numbers

    `UNUR_TEST_N_PDF'
          count number of PDF calls

    `UNUR_TEST_CHI2'
          run chi^2 test for goodness of fit

    `UNUR_TEST_SAMPLE'
          print a small sample.
     All these tests can be started individually (see below).

 -- Function: void unur_test_printsample (UNUR_GEN* GENERATOR, int N_ROWS, int
          N_COLS, FILE* OUT)
     Print a small sample with N_ROWS rows and N_COLS columns.  OUT is the
     output stream to which all results are written.

 -- Function: UNUR_GEN* unur_test_timing (UNUR_PAR* PARAMETERS, int
          LOG10_SAMPLESIZE, double* TIME_SETUP, double* TIME_SAMPLE, int
          VERBOSITY, FILE* OUT)
     Timing. PARAMETERS is an parameter object for which setup time and
     marginal generation times have to be measured. The results are written
     into TIME_SETUP and TIME_SAMPLE, respectively. LOG10_SAMPLESIZE is the
     common logarithm of the sample size that is used for timing.

     If VERBOSITY is `TRUE' then a small table is printed to output stream OUT
     with setup time, marginal generation time and average generation times for
     generating 10, 100, ... random variates. All times are given in micro
     seconds and relative to the generation times for the underlying uniform
     random number (using the UNIF interface) and an exponential distributed
     random variate using the inversion method.

     The created generator object is returned.  If a generator object could not
     be created successfully, then `NULL' is returned.

     If VERBOSITY is `TRUE' the result is written to the output stream OUT.

     Notice: All timing results are subject to heavy changes. Reruning timings
     usually results in different results. Minor changes in the source code can
     cause changes in such timings up to 25 percent.

 -- Function: double unur_test_timing_R (UNUR_PAR* PARAMETERS, const CHAR*
          DISTRSTR, const CHAR* METHODSTR, double LOG10_SAMPLESIZE, double*
          TIME_SETUP, double* TIME_MARGINAL)
     Timing. PARAMETERS is an parameter object for which setup time and
     marginal generation times have to be measured. The results are written
     into TIME_SETUP and TIME_MARGINAL, respectively. LOG10_SAMPLESIZE is the
     common logarithm of the sample size that is used for timing.

     Alternatively, one could provide the "parameter object" using strings
     DISTRSTR and METHODSTR as used for `unur_makegen_ssu'.  The results are
     more accurate than those of function `unur_test_timing' as the timings are
     computed using linear regression with several timings for sample size 1 and
     10^LOG10_SAMPLESIZE.  For each sample size total generation time
     (including setup) is measured 10 times. Since the these timings can be
     influenced by external effects (like disc sync or handling of interupts)
     the 2 fastest and the 3 slowest timings are discarded.  Intercept and
     slope for simple linear regression are than stored and R^2 is returned.

     In case of an error `-100.' is returned.

     Notice: All timing results are subject to heavy changes. Reruning timings
     usually results in different results. Minor changes in the source code can
     cause changes in such timings up to 25 percent.

 -- Function: double unur_test_timing_uniform (const UNUR_PAR* PARAMETERS, int
          LOG10_SAMPLESIZE)
 -- Function: double unur_test_timing_exponential (const UNUR_PAR* PARAMETERS,
          int LOG10_SAMPLESIZE)
     Marginal generation times for the underlying uniform random number (using
     the UNIF interface) and an exponential distributed random variate using
     the inversion method. These times are used in `unur_test_timing' to
     compute the relative timings results.

 -- Function: double unur_test_timing_total (const UNUR_PAR* PARAMETERS, int
          SAMPLESIZE, double AVG_DURATION)
     Timing. PARAMETERS is an parameter object for which average times a sample
     of size SAMPLESIZE (including setup) are estimated. Thus sampling is
     repeated and the median of these timings is returned (in micro seconds).
     The number of iterations is computed automatically such that the total
     amount of time necessary for the test ist approximately AVG_DURATION
     (given in seconds).  However, for very slow generator with expensive setup
     time the time necessary for this test may be (much) larger.

     If an error occurs then `-1' is returned.

     Notice: All timing results are subject to heavy changes. Reruning timings
     usually results in different results. Minor changes in the source code can
     cause changes in such timings up to 25 percent.

 -- Function: int unur_test_count_urn (UNUR_GEN* GENERATOR, int SAMPLESIZE, int
          VERBOSITY, FILE* OUT)
     Count used uniform random numbers. It returns the total number of uniform
     random numbers required for a sample of non-uniform random variates of
     size SAMPLESIZE. In case of an error `-1' is returned.

     If VERBOSITY is `TRUE' the result is written to the output stream OUT.

     _Notice:_ This test uses global variables to store counters. Thus it is
     not thread save.

 -- Function: int unur_test_count_pdf (UNUR_GEN* GENERATOR, int SAMPLESIZE, int
          VERBOSITY, FILE* OUT)
     Count evaluations of PDF and similar functions. It returns the total
     number of evaluations of all such functions required for a sample of
     non-uniform random variates of size SAMPLESIZE.  If VERBOSITY is `TRUE'
     then a more detailed report is printed to the output stream OUT.  In case
     of an error `-1' is returned.  This test is run on a copy of the given
     generator object.

     _Notice:_ The printed numbers of evaluation should be interpreted with
     care. For example, methods either use the PDF or the logPDF; if only the
     logPDF is given, but a method needs the PDF then both the logPDF and the
     PDF (a wrapper around the logPDF) are called and thus one call to the PDF
     is counted twice.

     _Notice:_ This test uses global variables to store function pointers and
     counters. Thus it is not thread save.

 -- Function: int unur_test_par_count_pdf (UNUR_PAR* PARAMETERS, int
          SAMPLESIZE, int VERBOSITY, FILE* OUT)
     Same as `unur_test_count_pdf' except that it is run on a parameter object.
     Thus it also prints the number of function evaluations for the setup. The
     temporary created generator object is destroyed before the results are
     returned.

 -- Function: double unur_test_chi2 (UNUR_GEN* GENERATOR, int INTERVALS, int
          SAMPLESIZE, int CLASSMIN, int VERBOSITY, FILE* OUT)
     Run a Chi^2 test with the GENERATOR.  The resulting p-value is returned.

     It works with discrete und continuous univariate distributions.  For the
     latter the CDF of the distribution is required.

     INTERVALS is the number of intervals that is used for continuous
     univariate distributions. SAMPLESIZE is the size of the sample that is
     used for testing. If it is set to `0' then a sample of size INTERVALS^2 is
     used (bounded to some upper bound).

     CLASSMIN is the minimum number of expected entries per class. If a class
     has to few entries then some classes are joined.

     VERBOSITY controls the output of the routine. If it is set to `1' then the
     result is written to the output stream OUT. If it is set to `2'
     additionally the list of expected and observed data is printed.  If it is
     set to `3' then all generated numbers are printed.  There is no output
     when it is set to `0'.

     _Notice:_ For multivariate distributions the generated points are
     transformed by the inverse of the Cholesky factor of the covariance matrix
     and the mean vectors (if given for the underlying distribution). The
     marginal distributions of the transformed vectors are then tested against
     the marginal distribution given by a `unur_distr_cvec_set_marginals' or
     `unur_distr_cvec_set_marginal_array' call.  (Notice that these marginal
     distributions are never set by default for any of the distributions
     provided by UNU.RAN.)  Then the Bonferroni corrected p-value of all these
     tests is returned.  However, the test may not be performed correctly if
     the domain of the underlying distribution is truncated by a
     `unur_distr_cvec_set_domain_rect' call and the components of the
     distribution are correlated (i.e.  `unur_distr_cvec_set_covar' is called
     with the non-NULL argument). Then it almost surely will fail.

 -- Function: int unur_test_moments (UNUR_GEN* GENERATOR, double* MOMENTS, int
          N_MOMENTS, int SAMPLESIZE, int VERBOSITY, FILE* OUT)
     Computes the first N_MOMENTS central moments for a sample of size
     SAMPLESIZE. The result is stored into the array MOMENTS.  N_MOMENTS must
     be an integer between `1' and `4'.  For multivariate distributions the
     moments are stored consecutively for each dimension and the provided
     MOMENTS-array must have a length of at least (N_MOMENTS+1) * DIM, where DIM
     is the dimension of the multivariate distribution.  The M'th moment for
     the D'th dimension (0<=D<DIM) is thus stored in the array element
     MOMENTS[D*N_MOMENTS+M]

     If VERBOSITY is `TRUE' the result is written to the output stream OUT.

 -- Function: double unur_test_correlation (UNUR_GEN* GENERATOR1, UNUR_GEN*
          GENERATOR2, int SAMPLESIZE, int VERBOSITY, FILE* OUT)
     Compute the correlation coefficient between streams from GENERATOR1 and
     GENERATOR2 for two samples of size SAMPLESIZE.  The resultung correlation
     is returned.

     If VERBOSITY is `TRUE' the result is written to the output stream OUT.

 -- Function: int unur_test_quartiles (UNUR_GEN* GENERATOR, double* Q0, double*
          Q1, double* Q2, double* Q3, double* Q4, int SAMPLESIZE, int
          VERBOSITY, FILE* OUT)
     Estimate quartiles of sample of size SAMPLESIZE.  The resulting quantiles
     are stored in the variables Q:
    Q0
          minimum

    Q1
          25%

    Q2
          median (50%)

    Q3
          75%

    Q4
          maximum

     If VERBOSITY is `TRUE' the result is written to the output stream OUT.

 -- Function: double unur_test_u_error (const UNUR_GEN* GENERATOR, double*
          MAX_ERROR, double* MAE, double THRESHOLD, int SAMPLESIZE, int
          RANDOMIZED, int TESTTAILS, int VERBOSITY, FILE* OUT)
     Estimate U-error of an inversion method, i.e.  error = | CDF^(-1)(U) - U |
     , by means of a simple Monte Carlo method.  Maximum and mean absolute
     errors are stored in MAX_ERROR and MAE, respectively.  The particular
     computed U-errors should not exceed the given THRESHOLD. However,
     approximization and round-off errors might occasionally trigger such an
     event.  Thus the function returns a penalty score. It is `0.' when the
     U-error never exceed the THRESHOLD value. It roughly gives the portion of
     particular test points where the U-error is too larger.  However, each
     such event is weighted with 1 + 10 x(uerror - threshold) / threshold .

     If RANDOMIZED is `TRUE' a pseudo-random sequence is used for the
     estimation.

     If RANDOMIZED is `FALSE' then the U-values are choosen equidistributed.
     If in addition RANDOMIZED is set to `TRUE' then the tails of the
     distributions are tested with a more dense set of points.

     If VERBOSITY is `TRUE' the result is written to the output stream OUT.

     When the domain of the distribution is truncated then the u-error might be
     larger due to rescaling of floating point numbers. Thus the observed
     u-errors are corrected by the corresponding rescaling factor.

     The test also works for discrete distributions albeit with some
     restrictions: It does not work correctly with truncated distributions and
     the TESTTAILS flag is ignored.  Moreover, the value stored in MAE is
     rather useless.

     In case of an error a negative value is returned.

10 Miscelleanous
****************

10.1 Mathematics
================

The following macros have been defined

`UNUR_INFINITY'
     indicates infinity for floating point numbers (of type `double').
     Internally `HUGE_VAL' is used.

`INT_MAX'
`INT_MIN'
     indicate infinity and minus infinity, resp., for integers (defined by ISO
     C standard).

``TRUE''
``FALSE''
     bolean expression for return values of `set' functions.

Appendix A A Short Introduction to Random Variate Generation
************************************************************

Random variate generation is the small field of research that deals with
algorithms to generate random variates from various distributions. It is common
to assume that a uniform random number generator is available. This is a
program that produces a sequence of independent and identically  distributed
continuous U(0,1) random variates (i.e. uniform random variates on the interval
(0,1) ). Of course real world computers can never generate ideal random numbers
and they cannot produce numbers of arbitrary precision but state-of-the-art
uniform random number generators come close to this aim. Thus random variate
generation deals with the problem of transforming such a sequence of U(0,1)
random numbers into non-uniform random variates.

   Here we shortly explain the basic ideas of the _inversion_, _rejection_, and
the _ratio of uniforms_ method. How these ideas can be used to design a
particular automatic random variate generation algorithms that can be applied
to large classes of distributions is shortly explained in the description of the
different methods included in this manual.

   For a deeper treatment of the ideas presented here, for other basic methods
and for automatic generators we refer the interested reader to our book [HLD04].

A.1 The Inversion Method
========================

When the inverse F^(-1) of the cumulative distribution function is known, then
random variate generation is easy.  We just generate a uniformly U(0,1)
distributed random number U and return

     X=F^(-1)(U).

The following figure shows how the inversion method works for the exponential
distribution.




  ^
1 +
  |                                                     ***************
  +--------------------------------------***************
  |                               *******  |
  |                          *****         |
  |                       ***              |
  |                    ***                 |
  |                  **                    |
  |                **                      |
  |              **                        |
  |             *                          |
  |           **                           |
  |          *                             |
  |         *                              |
  |        *                               |
  |       *                                |
  +------*                                 |
  |     *|                                 |
  |    * |                                 |
  |   *  |                                 |
  |   *  |                                 |
  |  *   |                                 |
  |  *   |                                 |
  | *    |                                 |
  |*     |                                 |
  |      V                                 V
  +------+------+-------------+------------++-------------+-------------+--->
  0             1             2             3             4             5


   This algorithm is so simple that inversion is certainly the method of choice
if the inverse CDF is available in closed form. This is the case e.g. for the
exponential and the Cauchy distribution.

   The inversion method also has other special advantages that make it even
more attractive for simulation purposes.  It preserves the structural
properties of the underlying uniform pseudo-random number generator.
Consequently it can be used, e.g., for variance reduction techniques, it is
easy to sample from truncated distributions, from marginal distributions, and
from order statistics.  Moreover, the quality of the generated random variables
depends only on the underlying uniform (pseudo-) random number generator.
Another important advantage of the inversion method is that we can easily
characterize its performance. To generate one random variate we always need
exactly one uniform variate and one evaluation of the inverse CDF. So its speed
mainly depends on the costs for evaluating the inverse CDF. Hence inversion is
often considered as the method of choice in the simulation literature.

   Unfortunately computing the inverse CDF is, for many important standard
distributions (e.g. for normal, student, gamma, and beta-distributions),
comparatively difficult and slow. Often no such routines are available in
standard programming libraries.  Then numerical methods for inverting the CDF
are necessary, e.g.  Newton's method or (polynomial or rational) approximations
of the inverse CDF. Such procedures, however, have the disadvantage that they
are not exact and/or slow.  UNU.RAN implements several methods: NINV (*note
NINV::), HINV (*note HINV::) and PINV (*note PINV::).

   For such approximate inversion methods the approximation error is important
for the quality of the generated point set.  Let X=G^(-1)(U) denote the
approximate inverse CDF, and let F and F^(-1) be the exact CDF and inverse CDF
of the distribution, resp.  There are three measures for the approximation
error:

_u-error_
     is given by

          uerror = |U-F(G^(-1)(U))|

     Goodness-of-fit tests like the Kolmogorov-Smirnov test or the chi-squared
     test look at this type of error.  We are also convinced that it is the
     most suitable error measure for Monte Carlo simulations as pseudo-random
     numbers and points of low discrepancy sets are located on a grid of
     restricted resolution.

_x-error_
     is given by

          absolute xerror = |F^(-1)(U)-G^(-1)(U)|


          relative xerror = |F^(-1)(U)-G^(-1)(U)| * |F^(-1)(U)|

     The x-error measure the deviation of G^(-1)(U) from the exact result. This
     measure is suitable when the inverse CDF is used as a quantile function in
     some computations.  The main problem with the x-error is that we have to
     use the _absolute x-error_ for X=F^(-1)(U) close to zero and the _relative
     x-error_ in the tails.


   We use the terms _u-resolution_ and _x-resolution_ as the maximal tolerated
u-error and x-error, resp.

   UNU.RAN allows to set u-resolution and x-resolution independently. Both
requirements must be fulfilled.  We use the following strategy for checking
whether the precision goal is reached:

_checking u-error:_
     The u-error must be slightly smaller than the given u-resolution:

          |U-F(G^(-1)(U))| < 0.9 * uresolution.

     There is no necessity to consinder the relative u-error as we have 0 < U <
     1.

_checking x-error:_
     We combine absoute and relative x-error and use the criterion

          |F^(-1)(U)-G^(-1)(U)| < xresolution * (|G^(-1)(U)| + xresolution).



   *Remark:* It should be noted here that the criterion based on the u-error is
too stringent whereever the CDF is extremely steep (and thus the PDF has a pole
or a high and narrow peak). This is in particular a problem for distributions
with a pole (e.g., the gamma distribution with shape parameter less than 0.5).
On the other hand using a criterion based on the x-error causes problems
whereever the CDF is extremly flat. This is in particular the case in the (far)
tails of heavy-tailed distributions (e.g., for the Cauchy distribution).

A.2 The Rejection Method
========================

The rejection method, often called _acceptance-rejection method_, has been
suggested by John von Neumann in 1951.  Since then it has proven to be the most
flexible and most efficient method to generate variates from continuous
distributions.

   We explain the rejection principle first for the density f(x) = sin(x)/2 on
the interval (0, pi).  To generate random variates from this distribution we
also can sample random points that are uniformly distributed in the region
between the graph of f(x) and the x-axis, i.e., the shaded region in the below
figure.



    ^
    |
    +------------------------------**+***------------------------------+
    |                         o****./.\..****            o             |
    |                        **..../...\.....**                        |
    |                      **...../.....\......**                      |
    |                    **...../.........\......**                    |
    |                   *....../...........\.......*                o  |
    |       o          *....../..............\......*                  |
    |                **...../......X..........\......**                |
    |               *....../...................\.......*    o          |
    |              *....../......................\......*              |
    |             *...../.........................\......*             |
    |            *..X../...........................\......*            |
    |           *...../..................X...........\.....*           |
    |          *..../.................................\.....*          |
    |         *..../.................................X..\....*    o    |
    |        *..../........X.............................\....*        |
    |       *.../.................................X.......\....*       |
    |      *.../............................................\...*      |
    |     *.../....................................X.........\...*     |
    |    *../...........................X.....................\...*    |
    |    *./............X.......................................\..*   |
    | o *./......................................................\.*   |
    |  */.........................................................\.*  |
    | */.....................................................X......\* |
    |*/......................................X.......................\*|
    *---------------+---+--+-------+----++---+----++-+-------+---------*---->

   In general this is not a trivial task but in this example we can easily use
the rejection trick: Sample a random point (X,Y) uniformly in the bounding
rectangle (0, pi)x(0,0.5).  This is easy since each coordinate can be sampled
independently from the respective uniform distributions U(0, pi) and U(0,0.5).
Whenever the point falls into the shaded region below the graph (indicated by
dots in the figure), i.e., when Y < sin(X)/2, we accept it and return X as a
random variate from the distribution with density f(x).  Otherwise we have to
reject the point (indicated by small circles in the figure), and try again.

   It is quite clear that this idea works for every distribution with a bounded
density on a bounded domain. Moreover, we can use this procedure with any
multiple of the density, i.e., with any positive bounded function with bounded
integral and it is not necessary to know the integral of this function. So we
use the term density in the sequel for any positive function with bounded
integral.

   From the figure we can conclude that the performance of a rejection
algorithm depends heavily on the area of the enveloping rectangle. Moreover,
the method does not work if the target distribution has infinite tails (or is
unbounded). Hence non-rectangular shaped regions for the envelopes are
important and we have to solve the problem of sampling points uniformly from
such domains. Looking again at the example above we notice that the
x-coordinate of the random point (X,Y) was sampled by inversion from the
uniform distribution on the domain of the given density. This motivates us to
replace the density of the uniform distribution by the (multiple of a) density
h(x) of some other appropriate distribution. We only have to take care that it
is chosen such that it is always an upper bound, i.e., h(x) >= f(x) for all x
in the domain of the distribution. To generate the pair (X,Y) we generate X
from the distribution with density proportional to h(x) and Y uniformly between
0 and h(X).  The first step (generate X ) is usually done by inversion (*note
Inversion::).

   Thus the general rejection algorithm for a hat h(x) with inverse CDF H^(-1)
consists of the following steps:
  1. Generate a U(0,1) random number U.

  2. Set X to H^(-1)(U).

  3. Generate a U(0,1) random number V.

  4. Set Y to V h(X).

  5. If Y <= f(X) accept X as the random variate.

  6. Else try again.

   If the evaluation of the density f(x) is expensive (i.e., time consuming) it
is possible to use a simple lower bound of the density as so called _squeeze
function_ s(x) (the triangular shaped function in the above figure is an
example for such a squeeze). We can then accept X when Y <= s(X) and can thus
often save the evaluation of the density.

   We have seen so far that the rejection principle leads to short and simple
generation algorithms. The main practical problem to apply the rejection
algorithm is the search for a good fitting hat function and for squeezes. We do
not discuss these topics here as they are the heart of the different automatic
algorithms implemented in UNU.RAN. Information about the construction of hat
and squeeze can therefore be found in the descriptions of the methods.

   The performance characteristics of rejection algorithms mainly depend on the
fit of the hat and the squeeze. It is not difficult to prove that:
   * The expected number of trials to generate one variate is the ratio between
     the area below the hat and the area below the density.

   * The expected number of evaluations of the density necessary to generate
     one variate is equal to the ratio between the area below the hat and the
     area below the density, when no squeeze is used.  Otherwise, when a
     squeeze is given it is equal to the ratio between the area between hat and
     squeeze and the area below the hat.

   * The `sqhratio' (i.e., the ratio between the area below the squeeze and the
     area below the hat) used in some of the UNU.RAN methods is easy to
     compute. It is useful as its reciprocal is an upper bound for the expected
     number of trials of the rejection algoritm. The expected number of
     evaluations of the density is bounded by (1/sqhratio)-1.

A.3 The Composition Method
==========================

The composition method is an important principle to facilitate and speed up
random variate generation. The basic idea is simple.  To generate random
variates with a given density we first split the domain of the density into
subintervals. Then we select one of these randomly with probabilities given by
the area below the density in the respective subintervals. Finally we generate a
random variate from the density of the selected part by inversion and return it
as random variate of the full distribution.

   Composition can be combined with rejection. Thus it is possible to decompose
the domain of the distribution into subintervals and to construct hat and
squeeze functions seperatly in every subinterval. The area below the hat must
be determined in every subinterval. Then the Composition rejection algorithm
contains the following steps:

  1. Generate the index J of the subinterval as the realisation of a discrete
     random variate with probabilities proportional to the area below the hat.

  2. Generate a random variate X proportional to the hat in interval J.

  3. Generate the U(0,f(X)) random number Y.

  4. If Y <= f(X) accept X as random variate.

  5. Else start again with generating the index J.

   The first step can be done in constant time (i.e., independent of the number
of chosen subintervals) by means of the indexed search method (*note
IndexedSearch::).

   It is possible to reduce the number of uniform random numbers required in
the above algorithm by recycling the random numbers used in Step 1 and
additionally by applying the principle of _immediate acceptance_.  For details
see [HLD04: Sect. 3.1] .

A.4 The Ratio-of-Uniforms Method
================================

The construction of an appropriate hat function for the given density is the
crucial step for constructing rejection algorithms.  Equivalently we can try to
find an appropriate envelope for the region between the graph of the density
and the x-axis, such that we can easily sample uniformly distributed random
points.  This task could become easier if we can find transformations that map
the region between the density and the axis into a region of more suitable
shape (for example into a bounded region).

   As a first example we consider the following simple algorithm for the Cauchy
distribution.
  1. Generate a U(-1,1) random number U and a U(0,1) random number V.

  2. If U^2 + V^2 <= 1 accept X=U/V as a Cauchy random variate.

  3. Else try again.

   It is possible to prove that the above algorithm indeed  generates Cauchy
random variates. The fundamental principle behind this algorithm is the fact
that the region below the density is mapped by the transformation

     (X,Y) -> (U,V)=(2 Xsqrt(Y),2 sqrt(Y))

into a half-disc in such a way that the ratio between the area of the image to
the area of the preimage is constant. This is due to the fact that that the
Jacobian of this transformation is constant.



                                      ^ v
                                      |
                                      |
   +---------------------------*-***********-*---------------------------+
   |                      *****.......|.......*****                      |
   |                  ****............|............****                  |
   |                **................|................**                |
   |             **...................|...................**             |
   |            *.....................|.....................*            |
   |          *.......................|.......................*          |
   |         *........................|.........................*        |
   |       *..........................|..........................*       |
   |      *...........................|...........................*      |
   |     *............................|............................*     |
   |    *.............................|.............................*    |
   |   *..............................|..............................*   |
   |  *...............................|...............................*  |
   |  *...............................|................................* |
   | *................................|................................* |
   | *................................|................................* |
   |*.................................|.................................*|
   |*.................................|.................................*|
   *..................................|..................................*
   *..................................|..................................*
   *..................................|..................................*
---*----------------------------------+----------------------------------*--> u

   The above example is a special case of a more general principle, called the
_Ratio-of-uniforms (RoU) method_. It is based on the fact that for a random
variable X with density f(x) and some constant mu we can generate X from the
desired density by calculating X=U/V+ mu for a pair (U,V) uniformly distributed
in the set

     A_f= { (u,v) : 0 < v <= sqrt(f(u/v+ mu)) } .

For most distributions it is best to set the constant mu equal to the mode of
the distribution. For sampling random points uniformly distributed in A_f
rejection from a convenient enveloping region is used, usually the minimal
bounding rectangle, i.e., the smallest possible rectangle that contains A_f
(see the above figure).  It is given by (u^-,u^+)x(0,v^+) where

     v^+ = sup_(b_l<x<b_r) sqrt(f(x)),
     u^- = inf_(b_l<x<b_r) (x- mu) sqrt(f(x)),
     u^+ = sup_(b_l<x<b_r) (x- mu) sqrt(f(x)).

Then the ratio-of-uniforms method consists of the following simple steps:
  1. Generate a U(u^-,u^+) random number U and a U(0,v^+) random number V.

  2. Set X to U/V+ mu.

  3. If V^2 <= f(X) accept X as the random variate.

  4. Else try again.

   To apply the ratio-of-uniforms algorithm to a certain density we have to
solve the simple optimization problems in the definitions above to obtain the
design constants u^-, u^+, and v^+.  This simple algorithm works for all
distributions with bounded densities that have subquadratic tails (i.e., tails
like 1/x^2 or lower). For most standard distributions it has quite good
rejection constants.  (E.g. 1.3688 for the normal and 1.4715 for the exponential
distribution.)

   Nevertheless, we use more sophisticated method that construct better fitting
envelopes, like method AROU (*note AROU::), or even avoid the computation of
these design constants and thus have almost no setup, like method SROU (*note
SROU::).

The Generalized Ratio-of-Uniforms Method
----------------------------------------

The Ratio-of-Uniforms method can be generalized: If a point (U,V) is uniformly
distributed in the set

     A_f= { (u,v) : 0 < v <= (f(u/v^r+ mu))^(1/(r+1)) }

then X=U/V^r+ mu has the denity f(x).  The minimal bounding rectangle of this
region is given by (u^-,u^+)x(0,v^+) where

     v^+ = sup_(b_l<x<b_r) (f(x))^(1/(r+1)),
     u^- = inf_(b_l<x<b_r) (x- mu) (f(x))^(r/(r+1)),
     u^+ = sup_(b_l<x<b_r) (x- mu) (f(x))^(r/(r+1)).

The above algorithm has then to be adjusted accordingly.  Notice that the
original Ratio-of-Uniforms method is the special case with r=1.

A.5 Inversion for Discrete Distributions
========================================

We have already presented the idea of the inversion method to generate from
continuous random variables (*note Inversion::). For a discrete random variable
X we can write it mathematically in the same way:

     X=F^(-1)(U),

where F is the CDF of the desired distribution and U is a uniform U(0,1) random
number. The difference compared to the continuous case is that F is now a
step-function. The following figure illustrates the idea of discrete inversion
for a simple distribution.



  ^
  |
1 +                                =======================
  |                                .
  |                                .
  |                                .
  |                     ===========.
  |                     .
  |                     .
  +---------------------+
  |                     |
  |                     |
  |          ===========|
  |          .          |
  |          .          |
  |          .          |
  |          .          |
  +==========.          |
  |                     V
  +----------+----------+----------+----------+----------+---->
  0          1          2          3          4          5

   To realize this idea on a computer we have to use a search algorithm. For
the simplest version called _Sequential Search_ the CDF is computed on-the-fly
as sum of the probabilities p(k), since this is usually much cheaper than
computing the CDF directly. It is obvious that the basic form of the search
algorithm only works for discrete random variables with probability mass
functions p(k) for nonnegative k.  The sequential search algorithm consists of
the following basic steps:

  1. Generate a U(0,1) random number U.

  2. Set X to 0 and P to p(0).

  3. Do while U > P

  4.       Set X to X+1 and P to P+p(X).

  5. Return X.

   With the exception of some very simple discrete distributions, sequential
search algorithms become very slow as the while-loop has to be repeated very
often. The expected number of iterations, i.e., the number of comparisons in
the while condition, is equal to the expectation of the distribution plus 1.
It can therefore become arbitrary large or even infinity if the tail of the
distribution is very heavy. Another serious problem can be critical round-off
errors due to summing up many probabilities p(k).  To speed up the search
procedure it is best to use indexed search.

A.6 Indexed Search (Guide Table Method)
=======================================

The idea to speed up the sequential search algorithm is easy to understand.
Instead of starting always at 0 we store a table of size C with starting points
for our search. For this table we compute F^(-1)(U) for C equidistributed
values of U, i.e., for u_i = i/C, i=0,...,C-1.  Such a table is called _guide
table_ or _hash table_.  Then it is easy to prove that for every U in (0,1) the
guide table entry for k=floor(UC) is bounded by F^(-1)(U).  This shows that we
can really start our sequential search procedure from the table entry for k and
the index k of the correct table entry can be found rapidly by means of the
truncation operation.

   The two main differences between _indexed search_ and _sequential search_
are that we start searching at the number determined by the guide table, and
that we have to compute and store the cumulative probabilities in the setup as
we have to know the cumulative probability for the starting point of the search
algorithm. The rounding problems that can occur in the sequential search
algorithm can occur here as well.  Compared to sequential search we have now
the obvious drawback of a slow setup. The computation of the cumulative
probabilities grows linear with the size of the domain of the distribution L.
What we gain is really high speed as the marginal execution time of the
sampling algorithm becomes very small. The expected number of comparisons is
bounded by 1+L/C.  This shows that there is a trade-off between speed and the
size of the guide table. Cache-effects in modern computers will however slow
down the speed-up for really large table sizes.  Thus we recommend to use a
guide table that is about two times larger than the probability vector to
obtain optimal speed.

Appendix B Glossary
*******************

*CDF*
     cumulative distribution function.

*HR*
     hazard rate (or failure rate).

*inverse local concavity*
     local concavity of inverse PDF f^(-1)(y) expressed in term of x =
     f^(-1)(y).  Is is given by ilc_f(x) = 1 + x f"(x) / f'(x)

*local concavity*
     maximum value of c such that PDF f(x) is T_c.  Is is given by lc_f(x) = 1
     - f"(x) f(x) / f'(x)^2

*PDF*
     probability density function.

*dPDF*
     derivative (gradient) of probability density function.

*PMF*
     probability mass function.

*PV*
     (finite) probability vector.

*URNG*
     uniform random number generator.

*U(a,b)*
     continuous uniform distribution on the interval (a,b).

*T-concave*
     a function f(x) is called T-convace if the transformed function T(f(x)) is
     concave.  We only deal with transformations T_c, where
    `c = 0'
          T(x) = log(x)

    `c = -0.5'
          T(x) = -1/sqrt(x)

    `c != 0'
          T(x) = sign(x) * x^c

*u-error*
     for a given approximate inverse CDF X=G^(-1)(U) the u-error is given as

          uerror = |U-F(G^(-1)(U))|

     where F denotes the exact CDF.  Goodness-of-fit tests like the
     Kolmogorov-Smirnov test or the chi-squared test look at this type of error.
     See *note Inversion:: for more details.

*u-resolution*
     the maximal tolerated u-error for an approximate inverse CDF.

*x-error*
     for a given approximate inverse CDF X=G^(-1)(U) the x-error is given as

          xerror = |F^(-1)(U)-G^(-1)(U)|

     where F^(-1) denotes the exact inverse CDF.  The x-error measure the
     deviation of G^(-1)(U) from the exact result.  Notice that we have to
     distinguish between _absolute_ and _relative_ x-error. In UNU.RAN we use
     the absolute x-error near 0 and the relative x-error otherwise.  See *note
     Inversion:: for more details.

*x-resolution*
     the maximal tolerated x-error for an approximate inverse CDF.


Appendix C Bibliography
***********************

Standard Distributions
----------------------

[JKKa92]
     N.L. JOHNSON, S. KOTZ, AND A.W. KEMP (1992).  Univariate Discrete
     Distributions, 2nd edition, John Wiley & Sons, Inc., New York.

[JKBb94]
     N.L. JOHNSON, S. KOTZ, AND N. BALAKRISHNAN (1994).  Continuous Univariate
     Distributions, Volume 1, 2nd edition, John Wiley & Sons, Inc., New York.

[JKBc95]
     N.L. JOHNSON, S. KOTZ, AND N. BALAKRISHNAN (1995).  Continuous Univariate
     Distributions, Volume 2, 2nd edition, John Wiley & Sons, Inc., New York.

[JKBd97]
     N.L. JOHNSON, S. KOTZ, AND N. BALAKRISHNAN (1997).  Discrete Multivariate
     Distributions, John Wiley & Sons, Inc., New York.

[KBJe00]
     S. KOTZ, N. BALAKRISHNAN, AND N.L. JOHNSON (2000).  Continuous
     Multivariate Distributions, Volume 1: Models and Applications, John Wiley
     & Sons, Inc., New York.


Universal Methods - Surveys
---------------------------

[HLD04]
     W. HO"RMANN, J. LEYDOLD, AND G. DERFLINGER (2004).  Automatic Nonuniform
     Random Variate Generation, Springer, Berlin.


Universal Methods
-----------------

[AJa93]
     J.H. AHRENS (1993).  Sampling from general distributions by suboptimal
     division of domains, Grazer Math. Berichte 319, 30pp.

[AJa95]
     J.H. AHRENS (1995).  An one-table method for sampling from continuous and
     discrete distributions, Computing 54(2), pp. 127-146.

[CAa74]
     H.C. CHEN AND Y. ASAU (1974).  On generating random variates from an
     empirical distribution, AIIE Trans. 6, pp. 163-166.

[DHLa08]
     G. DERFLINGER, W. HO"RMANN, AND J. LEYDOLD (2008).  Numerical inversion
     when only the density function is known, Research Report Series of the
     Department of Statistics and Mathematics 78, WU Wien, Augasse 2-6, A-1090
     Wien, Austria, `http://epub.wu.ac.at/english/'.

[DLa86]
     L. DEVROYE (1986).  Non-Uniform Random Variate Generation, Springer
     Verlag, New York.

[GWa92]
     W.R. GILKS AND P. WILD (1992).  Adaptive rejection sampling for Gibbs
     sampling, Applied Statistics 41, pp. 337-348.

[HWa95]
     W. HO"RMANN (1995).  A rejection technique for sampling from T-concave
     distributions, ACM Trans. Math. Software 21(2), pp. 182-193.

[HDa96]
     W. HO"RMANN AND G. DERFLINGER (1996).  Rejection-inversion to generate
     variates from monotone discrete distributions, ACM TOMACS 6(3), 169-184.

[HLa00]
     W. HO"RMANN AND J. LEYDOLD (2000).  Automatic random variate generation
     for simulation input.  In: J.A. Joines, R. Barton, P. Fishwick, K. Kang
     (eds.), Proceedings of the 2000 Winter Simulation Conference, pp. 675-682.

[HLa03]
     W. HO"RMANN AND J. LEYDOLD (2003).  Continuous Random Variate Generation
     by Fast Numerical Inversion, ACM TOMACS 13(4), 347-362.

[HLDa07]
     W. HO"RMANN, J. LEYDOLD, AND G. DERFLINGER (2007).  Automatic Random
     Variate Generation for Unbounded Densities, ACM Trans. Model. Comput.
     Simul. 17(4), pp.18.

[KLPa05]
     R. KARAWATZKI, J. LEYDOLD, AND K. PO"TZELBERGER (2005).  Automatic Markov
     chain Monte Carlo procedures for sampling from multivariate distributions,
     Research Report Series of the Department of Statistics and Mathematics 27,
     WU Wien, Augasse 2-6, A-1090 Wien, Austria,
     `http://epub.wu.ac.at/english/'.

[LJa98]
     J. LEYDOLD (1998).  A Rejection Technique for Sampling from Log-Concave
     Multivariate Distributions, ACM TOMACS 8(3), pp. 254-280.

[LJa00]
     J. LEYDOLD (2000).  Automatic Sampling with the Ratio-of-Uniforms Method,
     ACM Trans. Math. Software 26(1), pp. 78-98.

[LJa01]
     J. LEYDOLD (2001).  A simple universal generator for continuous and
     discrete univariate T-concave distributions, ACM Trans. Math. Software
     27(1), pp. 66-82.

[LJa02]
     J. LEYDOLD (2003).  Short universal generators via generalized
     ratio-of-uniforms method, Math. Comp. 72(243), pp. 1453-1471.

[WGS91]
     J.C. WAKEFIELD, A.E. GELFAND, AND A.F.M. SMITH (1992).  Efficient
     generation of random variates via the ratio-of-uniforms method, Statist.
     Comput. 1(2), pp. 129-133.

[WAa77]
     A.J. WALKER (1977).  An efficient method for generating discrete random
     variables with general distributions, ACM Trans. Math. Software 3, pp.
     253-256.


Special Generators
------------------

[ADa74]
     J.H. AHRENS, U. DIETER (1974).  Computer methods for sampling from gamma,
     beta, Poisson and binomial distributions, Computing 12, 223-246.

[ADa82]
     J.H. AHRENS, U. DIETER (1982).  Generating gamma variates by a modified
     rejection technique, Communications of the ACM 25, 47-54.

[ADb82]
     J.H. AHRENS, U. DIETER (1982).  Computer generation of Poisson deviates
     from modified normal distributions, ACM Trans. Math. Software 8, 163-179.

[BMa58]
     G.E.P. BOX AND M.E. MULLER (1958).  A note on the generation of random
     normal deviates, Annals Math. Statist. 29, 610-611.

[CHa77]
     R.C.H. CHENG (1977).  The Generation of Gamma Variables with Non-Integral
     Shape Parameter, Appl. Statist. 26(1), 71-75.

[Dag89]
     J.S. DAGPUNAR (1989).  An Easily Implemented Generalised Inverse Gaussian
     Generator, Commun. Statist. Simul. 18(2), 703-710.

[HDa90]
     W. HO"RMANN AND G. DERFLINGER (1990).  The ACR Method for generating
     normal random variables, OR Spektrum 12, 181-185.

[KAa81]
     A.W. KEMP (1981).  Efficient generation of logarithmically distributed
     pseudo-random variables, Appl. Statist. 30, 249-253.

[KRa76]
     A.J. KINDERMAN AND J.G. RAMAGE (1976).  Computer Generation of Normal
     Random Variables, J. Am. Stat. Assoc. 71(356), 893 - 898.

[MJa87]
     J.F. MONAHAN (1987).  An algorithm for generating chi random variables,
     ACM Trans. Math. Software 13, 168-172.

[MGa62]
     G. MARSAGLIA (1962).  Improving the Polar Method for Generating a Pair of
     Random Variables, Boeing Sci. Res. Lab., Seattle, Washington.

[MOa84]
     G. MARSAGLIA AND I. OLKIN (1984).  Generating Correlation Matrices, SIAM
     J. Sci. Stat. Comput 5, 470-475.

[STa89]
     E. STADLOBER (1989).  Sampling from Poisson, binomial and hypergeometric
     distributions: ratio of uniforms as a simple and fast alternative, Bericht
     303, Math. Stat. Sektion, Forschungsgesellschaft Joanneum, Graz.

[ZHa94]
     H. ZECHNER (1994).  Efficient sampling from continuous and discrete
     unimodal distributions, Pd.D. Thesis, 156 pp., Technical University Graz,
     Austria.


Other references
----------------

[CPa76]
     R. CRANLEY AND T.N.L. PATTERSON (1976).  Randomization of number theoretic
     methods for multiple integration, SIAM J. Num. Anal., Vol. 13, pp. 904-914.

[HJa61]
     R. HOOKE AND T.A. JEEVES (1961).  Direct Search Solution of Numerical and
     Statistical Problems, Journal of the ACM, Vol. 8, April 1961, pp. 212-229.


Appendix D Function Index
*************************

FALSE:                                         See 10.1.            (line 13427)
INT_MAX:                                       See 10.1.            (line 13421)
INT_MIN:                                       See 10.1.            (line 13422)
TRUE:                                          See 10.1.            (line 13426)
unur_arou_chg_verify:                          See 5.3.1.           (line  5341)
unur_arou_get_hatarea:                         See 5.3.1.           (line  5307)
unur_arou_get_sqhratio:                        See 5.3.1.           (line  5303)
unur_arou_get_squeezearea:                     See 5.3.1.           (line  5311)
unur_arou_new:                                 See 5.3.1.           (line  5264)
unur_arou_set_cpoints:                         See 5.3.1.           (line  5323)
unur_arou_set_darsfactor:                      See 5.3.1.           (line  5282)
unur_arou_set_guidefactor:                     See 5.3.1.           (line  5333)
unur_arou_set_max_segments:                    See 5.3.1.           (line  5316)
unur_arou_set_max_sqhratio:                    See 5.3.1.           (line  5293)
unur_arou_set_pedantic:                        See 5.3.1.           (line  5349)
unur_arou_set_usecenter:                       See 5.3.1.           (line  5329)
unur_arou_set_usedars:                         See 5.3.1.           (line  5267)
unur_arou_set_verify:                          See 5.3.1.           (line  5340)
unur_ars_chg_reinit_ncpoints:                  See 5.3.2.           (line  5477)
unur_ars_chg_reinit_percentiles:               See 5.3.2.           (line  5461)
unur_ars_chg_verify:                           See 5.3.2.           (line  5491)
unur_ars_eval_invcdfhat:                       See 5.3.2.           (line  5523)
unur_ars_get_loghatarea:                       See 5.3.2.           (line  5518)
unur_ars_new:                                  See 5.3.2.           (line  5439)
unur_ars_set_cpoints:                          See 5.3.2.           (line  5450)
unur_ars_set_max_intervals:                    See 5.3.2.           (line  5442)
unur_ars_set_max_iter:                         See 5.3.2.           (line  5484)
unur_ars_set_pedantic:                         See 5.3.2.           (line  5499)
unur_ars_set_reinit_ncpoints:                  See 5.3.2.           (line  5475)
unur_ars_set_reinit_percentiles:               See 5.3.2.           (line  5459)
unur_ars_set_verify:                           See 5.3.2.           (line  5490)
unur_auto_new:                                 See 5.2.             (line  4993)
unur_auto_set_logss:                           See 5.2.             (line  4996)
unur_cext_get_distrparams:                     See 5.3.3.           (line  5750)
unur_cext_get_ndistrparams:                    See 5.3.3.           (line  5751)
unur_cext_get_params:                          See 5.3.3.           (line  5738)
unur_cext_new:                                 See 5.3.3.           (line  5713)
unur_cext_set_init:                            See 5.3.3.           (line  5717)
unur_cext_set_sample:                          See 5.3.3.           (line  5729)
unur_chg_debug:                                See 8.2.             (line 12860)
unur_chg_urng:                                 See 6.               (line 11089)
unur_chg_urng_aux:                             See 6.               (line 11118)
unur_chgto_urng_aux_default:                   See 6.               (line 11112)
unur_cstd_chg_truncated:                       See 5.3.4.           (line  5840)
unur_cstd_new:                                 See 5.3.4.           (line  5817)
unur_cstd_set_variant:                         See 5.3.4.           (line  5829)
unur_dari_chg_verify:                          See 5.8.1.           (line  9959)
unur_dari_new:                                 See 5.8.1.           (line  9925)
unur_dari_set_cpfactor:                        See 5.8.1.           (line  9950)
unur_dari_set_squeeze:                         See 5.8.1.           (line  9928)
unur_dari_set_tablesize:                       See 5.8.1.           (line  9940)
unur_dari_set_verify:                          See 5.8.1.           (line  9958)
unur_dau_new:                                  See 5.8.2.           (line 10010)
unur_dau_set_urnfactor:                        See 5.8.2.           (line 10013)
UNUR_DEBUG_ADAPT:                              See 8.2.             (line 12833)
UNUR_DEBUG_ALL:                                See 8.2.             (line 12824)
UNUR_DEBUG_INIT:                               See 8.2.             (line 12827)
UNUR_DEBUG_OFF:                                See 8.2.             (line 12821)
UNUR_DEBUG_SAMPLE:                             See 8.2.             (line 12836)
UNUR_DEBUG_SETUP:                              See 8.2.             (line 12830)
unur_dext_get_distrparams:                     See 5.8.3.           (line 10240)
unur_dext_get_ndistrparams:                    See 5.8.3.           (line 10241)
unur_dext_get_params:                          See 5.8.3.           (line 10228)
unur_dext_new:                                 See 5.8.3.           (line 10203)
unur_dext_set_init:                            See 5.8.3.           (line 10207)
unur_dext_set_sample:                          See 5.8.3.           (line 10219)
unur_dgt_new:                                  See 5.8.4.           (line 10316)
unur_dgt_set_guidefactor:                      See 5.8.4.           (line 10319)
unur_dgt_set_variant:                          See 5.8.4.           (line 10329)
unur_distr_:                                   See 7.               (line 11715)
unur_distr_beta:                               See 7.1.2.           (line 11827)
unur_distr_binomial:                           See 7.3.1.           (line 12574)
unur_distr_cauchy:                             See 7.1.3.           (line 11850)
unur_distr_cemp_get_data:                      See 4.4.             (line  3730)
unur_distr_cemp_new:                           See 4.4.             (line  3707)
unur_distr_cemp_read_data:                     See 4.4.             (line  3719)
unur_distr_cemp_set_data:                      See 4.4.             (line  3715)
unur_distr_cemp_set_hist:                      See 4.4.             (line  3738)
unur_distr_cemp_set_hist_bins:                 See 4.4.             (line  3768)
unur_distr_cemp_set_hist_domain:               See 4.4.             (line  3763)
unur_distr_cemp_set_hist_prob:                 See 4.4.             (line  3751)
unur_distr_chi:                                See 7.1.4.           (line 11876)
unur_distr_chisquare:                          See 7.1.5.           (line 11901)
unur_distr_condi_get_condition:                See 4.6.             (line  4396)
unur_distr_condi_get_distribution:             See 4.6.             (line  4404)
unur_distr_condi_new:                          See 4.6.             (line  4347)
unur_distr_condi_set_condition:                See 4.6.             (line  4376)
unur_distr_cont_eval_cdf:                      See 4.2.             (line  3253)
unur_distr_cont_eval_dlogpdf:                  See 4.2.             (line  3280)
unur_distr_cont_eval_dpdf:                     See 4.2.             (line  3251)
unur_distr_cont_eval_hr:                       See 4.2.             (line  3452)
unur_distr_cont_eval_invcdf:                   See 4.2.             (line  3255)
unur_distr_cont_eval_logcdf:                   See 4.2.             (line  3282)
unur_distr_cont_eval_logpdf:                   See 4.2.             (line  3278)
unur_distr_cont_eval_pdf:                      See 4.2.             (line  3249)
unur_distr_cont_get_cdf:                       See 4.2.             (line  3240)
unur_distr_cont_get_cdfstr:                    See 4.2.             (line  3304)
unur_distr_cont_get_center:                    See 4.2.             (line  3515)
unur_distr_cont_get_dlogpdf:                   See 4.2.             (line  3274)
unur_distr_cont_get_dlogpdfstr:                See 4.2.             (line  3378)
unur_distr_cont_get_domain:                    See 4.2.             (line  3403)
unur_distr_cont_get_dpdf:                      See 4.2.             (line  3238)
unur_distr_cont_get_dpdfstr:                   See 4.2.             (line  3303)
unur_distr_cont_get_hr:                        See 4.2.             (line  3445)
unur_distr_cont_get_hrstr:                     See 4.2.             (line  3468)
unur_distr_cont_get_invcdf:                    See 4.2.             (line  3242)
unur_distr_cont_get_logcdf:                    See 4.2.             (line  3276)
unur_distr_cont_get_logcdfstr:                 See 4.2.             (line  3382)
unur_distr_cont_get_logpdf:                    See 4.2.             (line  3272)
unur_distr_cont_get_logpdfstr:                 See 4.2.             (line  3376)
unur_distr_cont_get_mode:                      See 4.2.             (line  3495)
unur_distr_cont_get_pdf:                       See 4.2.             (line  3236)
unur_distr_cont_get_pdfarea:                   See 4.2.             (line  3541)
unur_distr_cont_get_pdfparams:                 See 4.2.             (line  3338)
unur_distr_cont_get_pdfparams_vec:             See 4.2.             (line  3365)
unur_distr_cont_get_pdfstr:                    See 4.2.             (line  3302)
unur_distr_cont_get_truncated:                 See 4.2.             (line  3409)
unur_distr_cont_new:                           See 4.2.             (line  3182)
unur_distr_cont_set_cdf:                       See 4.2.             (line  3193)
unur_distr_cont_set_cdfstr:                    See 4.2.             (line  3297)
unur_distr_cont_set_center:                    See 4.2.             (line  3504)
unur_distr_cont_set_dlogpdf:                   See 4.2.             (line  3268)
unur_distr_cont_set_domain:                    See 4.2.             (line  3387)
unur_distr_cont_set_dpdf:                      See 4.2.             (line  3191)
unur_distr_cont_set_hr:                        See 4.2.             (line  3418)
unur_distr_cont_set_hrstr:                     See 4.2.             (line  3459)
unur_distr_cont_set_invcdf:                    See 4.2.             (line  3195)
unur_distr_cont_set_logcdf:                    See 4.2.             (line  3270)
unur_distr_cont_set_logcdfstr:                 See 4.2.             (line  3380)
unur_distr_cont_set_logpdf:                    See 4.2.             (line  3266)
unur_distr_cont_set_logpdfstr:                 See 4.2.             (line  3374)
unur_distr_cont_set_mode:                      See 4.2.             (line  3480)
unur_distr_cont_set_pdf:                       See 4.2.             (line  3189)
unur_distr_cont_set_pdfarea:                   See 4.2.             (line  3520)
unur_distr_cont_set_pdfparams:                 See 4.2.             (line  3311)
unur_distr_cont_set_pdfparams_vec:             See 4.2.             (line  3346)
unur_distr_cont_set_pdfstr:                    See 4.2.             (line  3286)
unur_distr_cont_upd_mode:                      See 4.2.             (line  3488)
unur_distr_cont_upd_pdfarea:                   See 4.2.             (line  3528)
unur_distr_copula:                             See 7.2.1.           (line 12472)
unur_distr_corder_eval_cdf:                    See 4.3.             (line  3619)
unur_distr_corder_eval_dpdf:                   See 4.3.             (line  3617)
unur_distr_corder_eval_pdf:                    See 4.3.             (line  3615)
unur_distr_corder_get_cdf:                     See 4.3.             (line  3607)
unur_distr_corder_get_distribution:            See 4.3.             (line  3579)
unur_distr_corder_get_domain:                  See 4.3.             (line  3646)
unur_distr_corder_get_dpdf:                    See 4.3.             (line  3605)
unur_distr_corder_get_mode:                    See 4.3.             (line  3670)
unur_distr_corder_get_pdf:                     See 4.3.             (line  3603)
unur_distr_corder_get_pdfarea:                 See 4.3.             (line  3688)
unur_distr_corder_get_pdfparams:               See 4.3.             (line  3635)
unur_distr_corder_get_rank:                    See 4.3.             (line  3593)
unur_distr_corder_get_truncated:               See 4.3.             (line  3651)
unur_distr_corder_new:                         See 4.3.             (line  3563)
unur_distr_corder_set_domain:                  See 4.3.             (line  3641)
unur_distr_corder_set_mode:                    See 4.3.             (line  3662)
unur_distr_corder_set_pdfarea:                 See 4.3.             (line  3675)
unur_distr_corder_set_pdfparams:               See 4.3.             (line  3630)
unur_distr_corder_set_rank:                    See 4.3.             (line  3586)
unur_distr_corder_upd_mode:                    See 4.3.             (line  3665)
unur_distr_corder_upd_pdfarea:                 See 4.3.             (line  3679)
unur_distr_correlation:                        See 7.4.1.           (line 12730)
unur_distr_cvec_eval_dlogpdf:                  See 4.5.             (line  3960)
unur_distr_cvec_eval_dpdf:                     See 4.5.             (line  3921)
unur_distr_cvec_eval_logpdf:                   See 4.5.             (line  3958)
unur_distr_cvec_eval_pdf:                      See 4.5.             (line  3909)
unur_distr_cvec_eval_pdlogpdf:                 See 4.5.             (line  3962)
unur_distr_cvec_eval_pdpdf:                    See 4.5.             (line  3933)
unur_distr_cvec_get_center:                    See 4.5.             (line  4285)
unur_distr_cvec_get_cholesky:                  See 4.5.             (line  4050)
unur_distr_cvec_get_covar:                     See 4.5.             (line  4048)
unur_distr_cvec_get_covar_inv:                 See 4.5.             (line  4052)
unur_distr_cvec_get_dlogpdf:                   See 4.5.             (line  3956)
unur_distr_cvec_get_dpdf:                      See 4.5.             (line  3902)
unur_distr_cvec_get_logpdf:                    See 4.5.             (line  3954)
unur_distr_cvec_get_marginal:                  See 4.5.             (line  4140)
unur_distr_cvec_get_mean:                      See 4.5.             (line  3979)
unur_distr_cvec_get_mode:                      See 4.5.             (line  4257)
unur_distr_cvec_get_pdf:                       See 4.5.             (line  3895)
unur_distr_cvec_get_pdfparams:                 See 4.5.             (line  4170)
unur_distr_cvec_get_pdfparams_vec:             See 4.5.             (line  4207)
unur_distr_cvec_get_pdfvol:                    See 4.5.             (line  4306)
unur_distr_cvec_get_rankcorr:                  See 4.5.             (line  4089)
unur_distr_cvec_get_rk_cholesky:               See 4.5.             (line  4091)
unur_distr_cvec_is_indomain:                   See 4.5.             (line  4236)
unur_distr_cvec_new:                           See 4.5.             (line  3836)
unur_distr_cvec_set_center:                    See 4.5.             (line  4268)
unur_distr_cvec_set_covar:                     See 4.5.             (line  3987)
unur_distr_cvec_set_covar_inv:                 See 4.5.             (line  4025)
unur_distr_cvec_set_dlogpdf:                   See 4.5.             (line  3950)
unur_distr_cvec_set_domain_rect:               See 4.5.             (line  4216)
unur_distr_cvec_set_dpdf:                      See 4.5.             (line  3863)
unur_distr_cvec_set_logpdf:                    See 4.5.             (line  3948)
unur_distr_cvec_set_marginal_array:            See 4.5.             (line  4109)
unur_distr_cvec_set_marginal_list:             See 4.5.             (line  4119)
unur_distr_cvec_set_marginals:                 See 4.5.             (line  4102)
unur_distr_cvec_set_mean:                      See 4.5.             (line  3966)
unur_distr_cvec_set_mode:                      See 4.5.             (line  4246)
unur_distr_cvec_set_pdf:                       See 4.5.             (line  3848)
unur_distr_cvec_set_pdfparams:                 See 4.5.             (line  4147)
unur_distr_cvec_set_pdfparams_vec:             See 4.5.             (line  4178)
unur_distr_cvec_set_pdfvol:                    See 4.5.             (line  4293)
unur_distr_cvec_set_pdlogpdf:                  See 4.5.             (line  3952)
unur_distr_cvec_set_pdpdf:                     See 4.5.             (line  3880)
unur_distr_cvec_set_rankcorr:                  See 4.5.             (line  4065)
unur_distr_cvec_upd_mode:                      See 4.5.             (line  4251)
unur_distr_cvec_upd_pdfvol:                    See 4.5.             (line  4297)
unur_distr_cvemp_get_data:                     See 4.7.             (line  4449)
unur_distr_cvemp_new:                          See 4.7.             (line  4418)
unur_distr_cvemp_read_data:                    See 4.7.             (line  4437)
unur_distr_cvemp_set_data:                     See 4.7.             (line  4429)
unur_distr_discr_eval_cdf:                     See 4.9.             (line  4617)
unur_distr_discr_eval_invcdf:                  See 4.9.             (line  4629)
unur_distr_discr_eval_pmf:                     See 4.9.             (line  4615)
unur_distr_discr_eval_pv:                      See 4.9.             (line  4613)
unur_distr_discr_get_cdfstr:                   See 4.9.             (line  4654)
unur_distr_discr_get_domain:                   See 4.9.             (line  4700)
unur_distr_discr_get_mode:                     See 4.9.             (line  4723)
unur_distr_discr_get_pmfparams:                See 4.9.             (line  4680)
unur_distr_discr_get_pmfstr:                   See 4.9.             (line  4653)
unur_distr_discr_get_pmfsum:                   See 4.9.             (line  4752)
unur_distr_discr_get_pv:                       See 4.9.             (line  4577)
unur_distr_discr_make_pv:                      See 4.9.             (line  4555)
unur_distr_discr_new:                          See 4.9.             (line  4523)
unur_distr_discr_set_cdf:                      See 4.9.             (line  4586)
unur_distr_discr_set_cdfstr:                   See 4.9.             (line  4649)
unur_distr_discr_set_domain:                   See 4.9.             (line  4686)
unur_distr_discr_set_invcdf:                   See 4.9.             (line  4607)
unur_distr_discr_set_mode:                     See 4.9.             (line  4712)
unur_distr_discr_set_pmf:                      See 4.9.             (line  4584)
unur_distr_discr_set_pmfparams:                See 4.9.             (line  4660)
unur_distr_discr_set_pmfstr:                   See 4.9.             (line  4639)
unur_distr_discr_set_pmfsum:                   See 4.9.             (line  4732)
unur_distr_discr_set_pv:                       See 4.9.             (line  4540)
unur_distr_discr_upd_mode:                     See 4.9.             (line  4715)
unur_distr_discr_upd_pmfsum:                   See 4.9.             (line  4740)
unur_distr_exponential:                        See 7.1.6.           (line 11922)
unur_distr_extremeI:                           See 7.1.7.           (line 11948)
unur_distr_extremeII:                          See 7.1.8.           (line 11974)
unur_distr_F:                                  See 7.1.1.           (line 11806)
unur_distr_free:                               See 4.1.             (line  3032)
unur_distr_gamma:                              See 7.1.9.           (line 12001)
unur_distr_geometric:                          See 7.3.2.           (line 12600)
unur_distr_get_dim:                            See 4.1.             (line  3043)
unur_distr_get_extobj:                         See 4.1.             (line  3112)
unur_distr_get_name:                           See 4.1.             (line  3037)
unur_distr_get_type:                           See 4.1.             (line  3053)
unur_distr_gig:                                See 7.1.10.          (line 12032)
unur_distr_gig2:                               See 7.1.11.          (line 12062)
unur_distr_hyperbolic:                         See 7.1.12.          (line 12087)
unur_distr_hypergeometric:                     See 7.3.3.           (line 12625)
unur_distr_ig:                                 See 7.1.13.          (line 12109)
unur_distr_is_cemp:                            See 4.1.             (line  3081)
unur_distr_is_cont:                            See 4.1.             (line  3075)
unur_distr_is_cvec:                            See 4.1.             (line  3078)
unur_distr_is_cvemp:                           See 4.1.             (line  3085)
unur_distr_is_discr:                           See 4.1.             (line  3089)
unur_distr_is_matr:                            See 4.1.             (line  3092)
unur_distr_laplace:                            See 7.1.14.          (line 12133)
unur_distr_logarithmic:                        See 7.3.4.           (line 12652)
unur_distr_logistic:                           See 7.1.15.          (line 12159)
unur_distr_lognormal:                          See 7.1.16.          (line 12185)
unur_distr_lomax:                              See 7.1.17.          (line 12207)
unur_distr_matr_get_dim:                       See 4.8.             (line  4482)
unur_distr_matr_new:                           See 4.8.             (line  4467)
unur_distr_multicauchy:                        See 7.2.2.           (line 12485)
unur_distr_multiexponential:                   See 7.2.3.           (line 12509)
unur_distr_multinormal:                        See 7.2.4.           (line 12527)
unur_distr_multistudent:                       See 7.2.5.           (line 12549)
unur_distr_negativebinomial:                   See 7.3.5.           (line 12678)
unur_distr_normal:                             See 7.1.18.          (line 12233)
unur_distr_pareto:                             See 7.1.19.          (line 12271)
unur_distr_poisson:                            See 7.3.6.           (line 12699)
unur_distr_powerexponential:                   See 7.1.20.          (line 12297)
unur_distr_rayleigh:                           See 7.1.21.          (line 12322)
unur_distr_set_extobj:                         See 4.1.             (line  3096)
unur_distr_set_name:                           See 4.1.             (line  3036)
unur_distr_slash:                              See 7.1.22.          (line 12342)
unur_distr_student:                            See 7.1.23.          (line 12366)
unur_distr_triangular:                         See 7.1.24.          (line 12389)
unur_distr_uniform:                            See 7.1.25.          (line 12416)
unur_distr_weibull:                            See 7.1.26.          (line 12442)
unur_dsrou_chg_cdfatmode:                      See 5.8.5.           (line 10407)
unur_dsrou_chg_verify:                         See 5.8.5.           (line 10399)
unur_dsrou_new:                                See 5.8.5.           (line 10385)
unur_dsrou_set_cdfatmode:                      See 5.8.5.           (line 10388)
unur_dsrou_set_verify:                         See 5.8.5.           (line 10398)
unur_dss_new:                                  See 5.8.6.           (line 10460)
unur_dstd_chg_truncated:                       See 5.8.7.           (line 10543)
unur_dstd_new:                                 See 5.8.7.           (line 10522)
unur_dstd_set_variant:                         See 5.8.7.           (line 10532)
unur_empk_chg_smoothing:                       See 5.4.1.           (line  8455)
unur_empk_chg_varcor:                          See 5.4.1.           (line  8468)
unur_empk_new:                                 See 5.4.1.           (line  8389)
unur_empk_set_beta:                            See 5.4.1.           (line  8443)
unur_empk_set_kernel:                          See 5.4.1.           (line  8392)
unur_empk_set_kernelgen:                       See 5.4.1.           (line  8419)
unur_empk_set_positive:                        See 5.4.1.           (line  8475)
unur_empk_set_smoothing:                       See 5.4.1.           (line  8453)
unur_empk_set_varcor:                          See 5.4.1.           (line  8467)
unur_empl_new:                                 See 5.4.2.           (line  8527)
UNUR_ERR_COMPILE:                              See 8.4.             (line 13083)
UNUR_ERR_COOKIE:                               See 8.4.             (line 13068)
UNUR_ERR_DISTR_DATA:                           See 8.4.             (line 12991)
UNUR_ERR_DISTR_DOMAIN:                         See 8.4.             (line 12976)
UNUR_ERR_DISTR_GEN:                            See 8.4.             (line 12979)
UNUR_ERR_DISTR_GET:                            See 8.4.             (line 12970)
UNUR_ERR_DISTR_INVALID:                        See 8.4.             (line 12988)
UNUR_ERR_DISTR_NPARAMS:                        See 8.4.             (line 12973)
UNUR_ERR_DISTR_PROP:                           See 8.4.             (line 12994)
UNUR_ERR_DISTR_REQUIRED:                       See 8.4.             (line 12982)
UNUR_ERR_DISTR_SET:                            See 8.4.             (line 12967)
UNUR_ERR_DISTR_UNKNOWN:                        See 8.4.             (line 12985)
UNUR_ERR_DOMAIN:                               See 8.4.             (line 13056)
UNUR_ERR_FSTR_DERIV:                           See 8.4.             (line 13052)
UNUR_ERR_FSTR_SYNTAX:                          See 8.4.             (line 13049)
UNUR_ERR_GEN:                                  See 8.4.             (line 13008)
UNUR_ERR_GEN_CONDITION:                        See 8.4.             (line 13014)
UNUR_ERR_GEN_DATA:                             See 8.4.             (line 13011)
UNUR_ERR_GEN_INVALID:                          See 8.4.             (line 13017)
UNUR_ERR_GEN_SAMPLING:                         See 8.4.             (line 13020)
UNUR_ERR_GENERIC:                              See 8.4.             (line 13071)
UNUR_ERR_INF:                                  See 8.4.             (line 13077)
UNUR_ERR_MALLOC:                               See 8.4.             (line 13062)
UNUR_ERR_NAN:                                  See 8.4.             (line 13080)
UNUR_ERR_NO_QUANTILE:                          See 8.4.             (line 13026)
UNUR_ERR_NO_REINIT:                            See 8.4.             (line 13023)
UNUR_ERR_NULL:                                 See 8.4.             (line 13065)
UNUR_ERR_PAR_INVALID:                          See 8.4.             (line 13004)
UNUR_ERR_PAR_SET:                              See 8.4.             (line 12998)
UNUR_ERR_PAR_VARIANT:                          See 8.4.             (line 13001)
UNUR_ERR_ROUNDOFF:                             See 8.4.             (line 13059)
UNUR_ERR_SHOULD_NOT_HAPPEN:                    See 8.4.             (line 13087)
UNUR_ERR_SILENT:                               See 8.4.             (line 13074)
UNUR_ERR_STR:                                  See 8.4.             (line 13037)
UNUR_ERR_STR_INVALID:                          See 8.4.             (line 13046)
UNUR_ERR_STR_SYNTAX:                           See 8.4.             (line 13043)
UNUR_ERR_STR_UNKNOWN:                          See 8.4.             (line 13040)
UNUR_ERR_URNG:                                 See 8.4.             (line 13030)
UNUR_ERR_URNG_MISS:                            See 8.4.             (line 13033)
unur_errno:                                    See 8.3.             (line 12944)
unur_free:                                     See 5.1.             (line  4891)
unur_gen_anti:                                 See 6.               (line 11216)
unur_gen_info:                                 See 5.1.             (line  4894)
unur_gen_nextsub:                              See 6.               (line 11218)
unur_gen_reset:                                See 6.               (line 11217)
unur_gen_resetsub:                             See 6.               (line 11219)
unur_gen_seed:                                 See 6.               (line 11215)
unur_gen_sync:                                 See 6.               (line 11214)
unur_get_default_urng:                         See 6.               (line 11065)
unur_get_default_urng_aux:                     See 6.               (line 11074)
unur_get_dimension:                            See 5.1.             (line  4903)
unur_get_distr:                                See 5.1.             (line  4910)
unur_get_errno:                                See 8.3.             (line 12947)
unur_get_genid:                                See 5.1.             (line  4907)
unur_get_stream:                               See 8.1.             (line 12792)
unur_get_strerror:                             See 8.3.             (line 12954)
unur_get_urng:                                 See 6.               (line 11094)
unur_get_urng_aux:                             See 6.               (line 11125)
unur_gibbs_chg_state:                          See 5.6.1.           (line  9177)
unur_gibbs_get_state:                          See 5.6.1.           (line  9175)
unur_gibbs_new:                                See 5.6.1.           (line  9111)
unur_gibbs_reset_state:                        See 5.6.1.           (line  9180)
unur_gibbs_set_burnin:                         See 5.6.1.           (line  9156)
unur_gibbs_set_c:                              See 5.6.1.           (line  9123)
unur_gibbs_set_startingpoint:                  See 5.6.1.           (line  9138)
unur_gibbs_set_thinning:                       See 5.6.1.           (line  9148)
unur_gibbs_set_variant_coordinate:             See 5.6.1.           (line  9114)
unur_gibbs_set_variant_random_direction:       See 5.6.1.           (line  9120)
unur_hinv_chg_truncated:                       See 5.3.5.           (line  6067)
unur_hinv_estimate_error:                      See 5.3.5.           (line  6090)
unur_hinv_eval_approxinvcdf:                   See 5.3.5.           (line  6057)
unur_hinv_get_n_intervals:                     See 5.3.5.           (line  6051)
unur_hinv_new:                                 See 5.3.5.           (line  5959)
unur_hinv_set_boundary:                        See 5.3.5.           (line  6022)
unur_hinv_set_cpoints:                         See 5.3.5.           (line  5995)
unur_hinv_set_guidefactor:                     See 5.3.5.           (line  6035)
unur_hinv_set_max_intervals:                   See 5.3.5.           (line  6043)
unur_hinv_set_order:                           See 5.3.5.           (line  5962)
unur_hinv_set_u_resolution:                    See 5.3.5.           (line  5980)
unur_hist_new:                                 See 5.4.3.           (line  8559)
unur_hitro_chg_state:                          See 5.6.2.           (line  9529)
unur_hitro_get_state:                          See 5.6.2.           (line  9527)
unur_hitro_new:                                See 5.6.2.           (line  9359)
unur_hitro_reset_state:                        See 5.6.2.           (line  9539)
unur_hitro_set_adaptive_multiplier:            See 5.6.2.           (line  9475)
unur_hitro_set_burnin:                         See 5.6.2.           (line  9513)
unur_hitro_set_r:                              See 5.6.2.           (line  9429)
unur_hitro_set_startingpoint:                  See 5.6.2.           (line  9489)
unur_hitro_set_thinning:                       See 5.6.2.           (line  9502)
unur_hitro_set_u:                              See 5.6.2.           (line  9453)
unur_hitro_set_use_adaptiveline:               See 5.6.2.           (line  9375)
unur_hitro_set_use_adaptiverectangle:          See 5.6.2.           (line  9403)
unur_hitro_set_use_boundingrectangle:          See 5.6.2.           (line  9391)
unur_hitro_set_v:                              See 5.6.2.           (line  9437)
unur_hitro_set_variant_coordinate:             See 5.6.2.           (line  9362)
unur_hitro_set_variant_random_direction:       See 5.6.2.           (line  9371)
unur_hrb_chg_verify:                           See 5.3.6.           (line  6160)
unur_hrb_new:                                  See 5.3.6.           (line  6150)
unur_hrb_set_upperbound:                       See 5.3.6.           (line  6154)
unur_hrb_set_verify:                           See 5.3.6.           (line  6159)
unur_hrd_chg_verify:                           See 5.3.7.           (line  6216)
unur_hrd_new:                                  See 5.3.7.           (line  6212)
unur_hrd_set_verify:                           See 5.3.7.           (line  6215)
unur_hri_chg_verify:                           See 5.3.8.           (line  6297)
unur_hri_new:                                  See 5.3.8.           (line  6284)
unur_hri_set_p0:                               See 5.3.8.           (line  6287)
unur_hri_set_verify:                           See 5.3.8.           (line  6296)
UNUR_INFINITY:                                 See 10.1.            (line 13417)
unur_init:                                     See 5.1.             (line  4806)
unur_itdr_chg_verify:                          See 5.3.9.           (line  6438)
unur_itdr_get_area:                            See 5.3.9.           (line  6426)
unur_itdr_get_cp:                              See 5.3.9.           (line  6421)
unur_itdr_get_ct:                              See 5.3.9.           (line  6422)
unur_itdr_get_xi:                              See 5.3.9.           (line  6420)
unur_itdr_new:                                 See 5.3.9.           (line  6391)
unur_itdr_set_cp:                              See 5.3.9.           (line  6402)
unur_itdr_set_ct:                              See 5.3.9.           (line  6411)
unur_itdr_set_verify:                          See 5.3.9.           (line  6429)
unur_itdr_set_xi:                              See 5.3.9.           (line  6394)
unur_makegen_dsu:                              See 3.               (line  1903)
unur_makegen_ssu:                              See 3.               (line  1901)
unur_mcorr_chg_eigenvalues:                    See 5.9.1.           (line 10658)
unur_mcorr_new:                                See 5.9.1.           (line 10642)
unur_mcorr_set_eigenvalues:                    See 5.9.1.           (line 10646)
unur_mixt_new:                                 See 5.11.1.          (line 10901)
unur_mixt_set_useinversion:                    See 5.11.1.          (line 10909)
unur_mvstd_new:                                See 5.5.1.           (line  8613)
unur_mvtdr_chg_verify:                         See 5.5.2.           (line  8745)
unur_mvtdr_get_hatvol:                         See 5.5.2.           (line  8740)
unur_mvtdr_get_ncones:                         See 5.5.2.           (line  8736)
unur_mvtdr_new:                                See 5.5.2.           (line  8703)
unur_mvtdr_set_boundsplitting:                 See 5.5.2.           (line  8713)
unur_mvtdr_set_maxcones:                       See 5.5.2.           (line  8722)
unur_mvtdr_set_stepsmin:                       See 5.5.2.           (line  8706)
unur_mvtdr_set_verify:                         See 5.5.2.           (line  8744)
unur_ninv_chg_max_iter:                        See 5.3.10.          (line  6557)
unur_ninv_chg_start:                           See 5.3.10.          (line  6593)
unur_ninv_chg_table:                           See 5.3.10.          (line  6610)
unur_ninv_chg_truncated:                       See 5.3.10.          (line  6614)
unur_ninv_chg_u_resolution:                    See 5.3.10.          (line  6572)
unur_ninv_chg_x_resolution:                    See 5.3.10.          (line  6563)
unur_ninv_eval_approxinvcdf:                   See 5.3.10.          (line  6633)
unur_ninv_new:                                 See 5.3.10.          (line  6538)
unur_ninv_set_max_iter:                        See 5.3.10.          (line  6556)
unur_ninv_set_start:                           See 5.3.10.          (line  6579)
unur_ninv_set_table:                           See 5.3.10.          (line  6597)
unur_ninv_set_u_resolution:                    See 5.3.10.          (line  6570)
unur_ninv_set_usebisect:                       See 5.3.10.          (line  6552)
unur_ninv_set_usenewton:                       See 5.3.10.          (line  6545)
unur_ninv_set_useregula:                       See 5.3.10.          (line  6541)
unur_ninv_set_x_resolution:                    See 5.3.10.          (line  6561)
unur_norta_new:                                See 5.5.3.           (line  8821)
unur_nrou_chg_verify:                          See 5.3.11.          (line  6780)
unur_nrou_new:                                 See 5.3.11.          (line  6730)
unur_nrou_set_center:                          See 5.3.11.          (line  6765)
unur_nrou_set_r:                               See 5.3.11.          (line  6758)
unur_nrou_set_u:                               See 5.3.11.          (line  6734)
unur_nrou_set_v:                               See 5.3.11.          (line  6749)
unur_nrou_set_verify:                          See 5.3.11.          (line  6771)
unur_pinv_estimate_error:                      See 5.3.12.          (line  7120)
unur_pinv_eval_approxcdf:                      See 5.3.12.          (line  7112)
unur_pinv_eval_approxinvcdf:                   See 5.3.12.          (line  7105)
unur_pinv_get_n_intervals:                     See 5.3.12.          (line  7089)
unur_pinv_new:                                 See 5.3.12.          (line  6938)
unur_pinv_set_boundary:                        See 5.3.12.          (line  7048)
unur_pinv_set_keepcdf:                         See 5.3.12.          (line  7093)
unur_pinv_set_max_intervals:                   See 5.3.12.          (line  7083)
unur_pinv_set_order:                           See 5.3.12.          (line  6941)
unur_pinv_set_searchboundary:                  See 5.3.12.          (line  7064)
unur_pinv_set_smoothness:                      See 5.3.12.          (line  6948)
unur_pinv_set_u_resolution:                    See 5.3.12.          (line  6994)
unur_pinv_set_use_upoints:                     See 5.3.12.          (line  7021)
unur_pinv_set_usecdf:                          See 5.3.12.          (line  7035)
unur_pinv_set_usepdf:                          See 5.3.12.          (line  7030)
unur_quantile:                                 See 5.1.             (line  4868)
unur_reinit:                                   See 5.1.             (line  4819)
unur_reset_errno:                              See 8.3.             (line 12950)
unur_run_tests:                                See 9.               (line 13163)
unur_sample_cont:                              See 5.1.             (line  4852)
unur_sample_discr:                             See 5.1.             (line  4851)
unur_sample_matr:                              See 5.1.             (line  4854)
unur_sample_urng:                              See 6.               (line 11139)
unur_sample_vec:                               See 5.1.             (line  4853)
unur_set_debug:                                See 8.2.             (line 12857)
unur_set_default_debug:                        See 8.2.             (line 12863)
unur_set_default_urng:                         See 6.               (line 11069)
unur_set_default_urng_aux:                     See 6.               (line 11073)
unur_set_error_handler:                        See 8.5.             (line 13136)
unur_set_error_handler_off:                    See 8.5.             (line 13147)
unur_set_stream:                               See 8.1.             (line 12778)
unur_set_urng:                                 See 6.               (line 11080)
unur_set_urng_aux:                             See 6.               (line 11098)
unur_set_use_distr_privatecopy:                See 5.1.             (line  4924)
unur_srou_chg_cdfatmode:                       See 5.3.13.          (line  7290)
unur_srou_chg_pdfatmode:                       See 5.3.13.          (line  7294)
unur_srou_chg_verify:                          See 5.3.13.          (line  7282)
unur_srou_new:                                 See 5.3.13.          (line  7224)
unur_srou_set_cdfatmode:                       See 5.3.13.          (line  7241)
unur_srou_set_pdfatmode:                       See 5.3.13.          (line  7248)
unur_srou_set_r:                               See 5.3.13.          (line  7227)
unur_srou_set_usemirror:                       See 5.3.13.          (line  7268)
unur_srou_set_usesqueeze:                      See 5.3.13.          (line  7258)
unur_srou_set_verify:                          See 5.3.13.          (line  7281)
unur_ssr_chg_cdfatmode:                        See 5.3.14.          (line  7408)
unur_ssr_chg_pdfatmode:                        See 5.3.14.          (line  7412)
unur_ssr_chg_verify:                           See 5.3.14.          (line  7400)
unur_ssr_new:                                  See 5.3.14.          (line  7373)
unur_ssr_set_cdfatmode:                        See 5.3.14.          (line  7376)
unur_ssr_set_pdfatmode:                        See 5.3.14.          (line  7383)
unur_ssr_set_usesqueeze:                       See 5.3.14.          (line  7391)
unur_ssr_set_verify:                           See 5.3.14.          (line  7399)
unur_str2distr:                                See 3.               (line  1894)
unur_str2gen:                                  See 3.               (line  1889)
UNUR_SUCCESS (0x0u):                           See 8.4.             (line 12963)
unur_tabl_chg_truncated:                       See 5.3.15.          (line  7686)
unur_tabl_chg_verify:                          See 5.3.15.          (line  7715)
unur_tabl_get_hatarea:                         See 5.3.15.          (line  7632)
unur_tabl_get_n_intervals:                     See 5.3.15.          (line  7647)
unur_tabl_get_sqhratio:                        See 5.3.15.          (line  7628)
unur_tabl_get_squeezearea:                     See 5.3.15.          (line  7636)
unur_tabl_new:                                 See 5.3.15.          (line  7522)
unur_tabl_set_areafraction:                    See 5.3.15.          (line  7563)
unur_tabl_set_boundary:                        See 5.3.15.          (line  7677)
unur_tabl_set_cpoints:                         See 5.3.15.          (line  7537)
unur_tabl_set_darsfactor:                      See 5.3.15.          (line  7589)
unur_tabl_set_guidefactor:                     See 5.3.15.          (line  7669)
unur_tabl_set_max_intervals:                   See 5.3.15.          (line  7641)
unur_tabl_set_max_sqhratio:                    See 5.3.15.          (line  7615)
unur_tabl_set_nstp:                            See 5.3.15.          (line  7544)
unur_tabl_set_pedantic:                        See 5.3.15.          (line  7723)
unur_tabl_set_slopes:                          See 5.3.15.          (line  7651)
unur_tabl_set_usedars:                         See 5.3.15.          (line  7574)
unur_tabl_set_useear:                          See 5.3.15.          (line  7553)
unur_tabl_set_variant_ia:                      See 5.3.15.          (line  7525)
unur_tabl_set_variant_splitmode:               See 5.3.15.          (line  7600)
unur_tabl_set_verify:                          See 5.3.15.          (line  7714)
unur_tdr_chg_reinit_ncpoints:                  See 5.3.16.          (line  7929)
unur_tdr_chg_reinit_percentiles:               See 5.3.16.          (line  7913)
unur_tdr_chg_truncated:                        See 5.3.16.          (line  7937)
unur_tdr_chg_verify:                           See 5.3.16.          (line  8014)
unur_tdr_eval_invcdfhat:                       See 5.3.16.          (line  8042)
unur_tdr_get_hatarea:                          See 5.3.16.          (line  7977)
unur_tdr_get_sqhratio:                         See 5.3.16.          (line  7973)
unur_tdr_get_squeezearea:                      See 5.3.16.          (line  7981)
unur_tdr_new:                                  See 5.3.16.          (line  7835)
unur_tdr_set_c:                                See 5.3.16.          (line  7838)
unur_tdr_set_cpoints:                          See 5.3.16.          (line  7903)
unur_tdr_set_darsfactor:                       See 5.3.16.          (line  7892)
unur_tdr_set_guidefactor:                      See 5.3.16.          (line  8006)
unur_tdr_set_max_intervals:                    See 5.3.16.          (line  7985)
unur_tdr_set_max_sqhratio:                     See 5.3.16.          (line  7963)
unur_tdr_set_pedantic:                         See 5.3.16.          (line  8022)
unur_tdr_set_reinit_ncpoints:                  See 5.3.16.          (line  7927)
unur_tdr_set_reinit_percentiles:               See 5.3.16.          (line  7911)
unur_tdr_set_usecenter:                        See 5.3.16.          (line  7992)
unur_tdr_set_usedars:                          See 5.3.16.          (line  7856)
unur_tdr_set_usemode:                          See 5.3.16.          (line  7995)
unur_tdr_set_variant_gw:                       See 5.3.16.          (line  7844)
unur_tdr_set_variant_ia:                       See 5.3.16.          (line  7852)
unur_tdr_set_variant_ps:                       See 5.3.16.          (line  7848)
unur_tdr_set_verify:                           See 5.3.16.          (line  8013)
unur_test_chi2:                                See 9.               (line 13300)
unur_test_correlation:                         See 9.               (line 13348)
unur_test_count_pdf:                           See 9.               (line 13275)
unur_test_count_urn:                           See 9.               (line 13264)
unur_test_moments:                             See 9.               (line 13335)
unur_test_par_count_pdf:                       See 9.               (line 13293)
unur_test_printsample:                         See 9.               (line 13186)
unur_test_quartiles:                           See 9.               (line 13357)
unur_test_timing:                              See 9.               (line 13192)
unur_test_timing_exponential:                  See 9.               (line 13241)
unur_test_timing_R:                            See 9.               (line 13216)
unur_test_timing_total:                        See 9.               (line 13248)
unur_test_timing_uniform:                      See 9.               (line 13239)
unur_test_u_error:                             See 9.               (line 13379)
unur_unif_new:                                 See 5.10.1.          (line 10680)
unur_urng_anti:                                See 6.               (line 11193)
unur_urng_free:                                See 6.               (line 11259)
unur_urng_fvoid_new:                           See 6.1.             (line 11351)
unur_urng_gsl_new:                             See 6.2.             (line 11463)
unur_urng_gslptr_new:                          See 6.2.             (line 11469)
unur_urng_gslqrng_new:                         See 6.3.             (line 11518)
unur_urng_new:                                 See 6.               (line 11241)
unur_urng_nextsub:                             See 6.               (line 11200)
unur_urng_prng_new:                            See 6.4.             (line 11566)
unur_urng_prngptr_new:                         See 6.4.             (line 11580)
unur_urng_randomshift_new:                     See 6.6.             (line 11693)
unur_urng_randomshift_nextshift:               See 6.6.             (line 11702)
unur_urng_reset:                               See 6.               (line 11158)
unur_urng_resetsub:                            See 6.               (line 11207)
unur_urng_rngstream_new:                       See 6.5.             (line 11635)
unur_urng_rngstreamptr_new:                    See 6.5.             (line 11639)
unur_urng_sample:                              See 6.               (line 11135)
unur_urng_sample_array:                        See 6.               (line 11144)
unur_urng_seed:                                See 6.               (line 11178)
unur_urng_set_anti:                            See 6.               (line 11285)
unur_urng_set_delete:                          See 6.               (line 11305)
unur_urng_set_nextsub:                         See 6.               (line 11295)
unur_urng_set_reset:                           See 6.               (line 11290)
unur_urng_set_resetsub:                        See 6.               (line 11300)
unur_urng_set_sample_array:                    See 6.               (line 11272)
unur_urng_set_seed:                            See 6.               (line 11281)
unur_urng_set_sync:                            See 6.               (line 11277)
unur_urng_sync:                                See 6.               (line 11172)
unur_use_urng_aux_default:                     See 6.               (line 11107)
unur_utdr_chg_pdfatmode:                       See 5.3.17.          (line  8150)
unur_utdr_chg_verify:                          See 5.3.17.          (line  8142)
unur_utdr_new:                                 See 5.3.17.          (line  8115)
unur_utdr_set_cpfactor:                        See 5.3.17.          (line  8127)
unur_utdr_set_deltafactor:                     See 5.3.17.          (line  8134)
unur_utdr_set_pdfatmode:                       See 5.3.17.          (line  8118)
unur_utdr_set_verify:                          See 5.3.17.          (line  8141)
unur_vempk_chg_smoothing:                      See 5.7.1.           (line  9694)
unur_vempk_chg_varcor:                         See 5.7.1.           (line  9707)
unur_vempk_new:                                See 5.7.1.           (line  9688)
unur_vempk_set_smoothing:                      See 5.7.1.           (line  9692)
unur_vempk_set_varcor:                         See 5.7.1.           (line  9706)
unur_vnrou_chg_u:                              See 5.5.4.           (line  8944)
unur_vnrou_chg_v:                              See 5.5.4.           (line  8954)
unur_vnrou_chg_verify:                         See 5.5.4.           (line  8974)
unur_vnrou_get_volumehat:                      See 5.5.4.           (line  8977)
unur_vnrou_new:                                See 5.5.4.           (line  8924)
unur_vnrou_set_r:                              See 5.5.4.           (line  8957)
unur_vnrou_set_u:                              See 5.5.4.           (line  8928)
unur_vnrou_set_v:                              See 5.5.4.           (line  8947)
unur_vnrou_set_verify:                         See 5.5.4.           (line  8965)
