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#### Error Function Inverses

##### Synopsis

    #include <boost/math/special_functions/erf.hpp>

    namespace boost{ namespace math{

    template <class T>
    calculated-result-type erf_inv(T p);

    template <class T, class Policy>
    calculated-result-type erf_inv(T p, const Policy&);

    template <class T>
    calculated-result-type erfc_inv(T p);

    template <class T, class Policy>
    calculated-result-type erfc_inv(T p, const Policy&);

    }} // namespaces

The return type of these functions is computed using the result type calculation rules: the return type is `double` if T is an integer type, and T otherwise.

The final Policy argument is optional and can be used to control the behaviour of the function: how it handles errors, what level of precision to use etc. Refer to the policy documentation for more details.

##### Description

    template <class T>
    calculated-result-type erf_inv(T z);

    template <class T, class Policy>
    calculated-result-type erf_inv(T z, const Policy&);

Returns the inverse error function of z, that is a value x such that:

    p = erf(x);

    template <class T>
    calculated-result-type erfc_inv(T z);

    template <class T, class Policy>
    calculated-result-type erfc_inv(T z, const Policy&);

Returns the inverse of the complement of the error function of z, that is a value x such that:

    p = erfc(x);

##### Accuracy

For types up to and including 80-bit long doubles the approximations used are accurate to less than ~ 2 epsilon. For higher precision types these functions have the same accuracy as the forward error functions.

##### Testing

There are two sets of tests:

- Basic sanity checks attempt to "round-trip" from x to p and back again. These tests have quite generous tolerances: in general both the error functions and their inverses change so rapidly in some places that round tripping to more than a couple of significant digits isn't possible. This is especially true when p is very near one: in this case there isn't enough "information content" in the input to the inverse function to get back where you started.
- Accuracy checks using high-precision test values. These measure the accuracy of the result, given exact input values.

##### Implementation

These functions use a rational approximation devised by JM to calculate an initial approximation to the result that is accurate to ~10-19, then only if that has insufficient accuracy compared to the epsilon for T, do we clean up the result using Halley iteration.

Constructing rational approximations to the erf/erfc functions is actually surprisingly hard, especially at high precision. For this reason no attempt has been made to achieve 10-34 accuracy suitable for use with 128-bit reals.

In the following discussion, p is the value passed to erf_inv, and q is the value passed to erfc_inv, so that p = 1 - q and q = 1 - p and in both cases we want to solve for the same result x.

For p < 0.5 the inverse erf function is reasonably smooth and the approximation:

    x = p(p + 10)(Y + R(p))

Gives a good result for a constant Y, and R(p) optimised for low absolute error compared to |Y|.

For q < 0.5 things get trickier, over the interval 0.5 > q > 0.25 the following approximation works well:

    x = sqrt(-2log(q)) / (Y + R(q))

While for q < 0.25, let

    z = sqrt(-log(q))

Then the result is given by:

    x = z(Y + R(z - B))

As before Y is a constant and the rational function R is optimised for low absolute error compared to |Y|. B is also a constant: it is the smallest value of z for which each approximation is valid. There are several approximations of this form each of which reaches a little further into the tail of the erfc function (at `long double` precision the extended exponent range compared to `double` means that the tail goes on for a very long way indeed).