[section:credits Credits and Acknowledgements]

Hubert Holin started the Boost.Math library.  The
Quaternions, Octonions, inverse
hyperbolic functions, and the sinus cardinal functions are his.

Daryle Walker wrote the integer gcd and lcm functions.

John Maddock started the special functions, the beta, gamma, erf, polynomial,
and factorial functions are his, as is the "Toolkit" section, and many
of the statistical distributions.

Paul A. Bristow threw down the challenge in
[@http://www2.open-std.org/JTC1/SC22/WG21/docs/papers/2004/n1668.pdf
A Proposal to add Mathematical Functions for Statistics to the C++
Standard Library] to add the key math functions, especially those essential for
statistics. After JM accepted and solved the difficult problems,
not only numerically, but in full C++ template style, PAB
implemented a few of the statistical distributions. PAB also tirelessly
proof-read everything that JM threw at him (so that all
remaining editorial mistakes are his fault).

Xiaogang Zhang worked on the Bessel functions and elliptic integrals for his
Google Summer of Code project 2006.

Bruno Lalande submitted the "compile time power of a runtime base" code.

Johan R'''&#xE5;'''de wrote the optimised floating-point classification
and manipulation code, and nonfinite facets to permit C99 output of infinities and NaNs.
(nonfinite facets were not added until Boost 1.47 but had been in use with Boost.Spirit).
This library was based on a suggestion from Robert Ramey, author of Boost.Serialization.
Paul A. Bristow expressed the need for better handling of
[@http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2006/n2022.pdf
Input & Output of NaN and infinity for the C++ Standard Library]
and suggested following the C99 format.

Antony Polukhin improved lexical cast avoiding stringstream so that
it was no longer necessary to use a globale C99 facet to handle nonfinites.

H'''&#xE5;'''kan Ard'''&#xF6;''',
Boris Gubenko, John Maddock,
Markus Sch'''&#xF6;'''pflin
and Olivier Verdier tested the floating-point library and
Martin Bonner, Peter Dimov and John Maddock provided valuable advice.

Gautam Sewani coded the logistic distribution as part of a Google Summer of Code project 2008.

M. A. (Thijs) van den Berg coded the Laplace distribution.
(Thijs has also threatened to implement some multivariate distributions).

Thomas Mang requested the inverse gamma in chi squared distributions
for Bayesian applications and helped in their implementation,
and provided a nice example of their use.

Professor Nico Temme for advice on the inverse incomplete beta function.

[@http://www.shoup.net Victor Shoup for NTL],
without which it would have much more difficult to
produce high accuracy constants, and especially
the tables of accurate values for testing.

We are grateful to Joel Guzman for helping us stress-test his
[@http://www.boost.org/tools/quickbook/index.htm Boost.Quickbook]
program used to generate the html and pdf versions
of this document, adding several new features en route.

Plots of the functions and distributions were prepared in
[@http://www.w3.org/ W3C] standard
[@http://www.svg.org/ Scalable Vector Graphic (SVG)] format
using a program created by Jacob Voytko during a
[@http://code.google.com/soc/2007/ Google Summer of Code (2007)].
From 2012, the latest versions of all Internet Browsers have support
for rendering SVG (with varying quality).  Older versions, especially
(Microsoft Internet Explorer (before IE 9) lack native SVG support
but can be made to work with
[@http://www.adobe.com/svg/viewer/install/ Adobe's free SVG viewer] plugin).
The SVG files can be converted to JPEG or PNG using
[@http://www.inkscape.org/ Inkscape].

We are also indebted to Matthias Schabel for managing the formal Boost-review
of this library, and to all the reviewers - including Guillaume Melquiond,
Arnaldur Gylfason, John Phillips, Stephan Tolksdorf and Jeff Garland
- for their many helpful comments.

Thanks to Mark Coleman and Georgi Boshnakov for spot test values
from __Mathematica, and of course,
to Eric Weisstein for nurturing __Mathworld, an invaluable resource.

The Skew-normal distribution and Owen's t function were written by Benjamin Sobotta.

We thank Thomas Mang for persuading us to allow t distributions
to have infinite degrees of freedom
and contributing to some long discussions about how to improve accuracy
for large non-centrality and/or large degrees of freedom.

Christopher Kormanyos wrote the e_float multiprecision library __TOMS910
which formed the basis for the Boost.Multiprecision library
which now can be used to allow most functions and distributions
to be computed up to a precision of the users' choice,
no longer restricted to built-in floating-point types like double.
(And thanks to Topher Cooper for bring Christopher's e_float to our attention).

Christopher Kormanyos wrote some examples for using __multiprecision,
and added methods for finding zeros of Bessel Functions.

Marco Guazzone provided the hyper-geometric distribution.

Rocco Romeo has found numerous small bugs and generally stress tested the
special functions code to near destruction!

Jeremy William Murphy added polynomial arithmetic tools.

Thomas Luu provided improvements to the quantile of the non-central chi squared distribution quantile.
and his thesis
* [@http://discovery.ucl.ac.uk/1482128/ Fast and accurate parallel computation of quantile functions for random number generation, 2016].

and his paper

Luu, Thomas; (2015), Efficient and Accurate Parallel Inversion of the Gamma Distribution,
SIAM Journal on Scientific Computing , 37 (1) C122 - C141,
[@http://dx.doi.org/10.1137/14095875X].

These also promise to help improve algorithms for computation of  quantile of several disitributions,
especially for parallel computation using GPUs.

[endsect] [/section:credits Credits and Acknowledgements]

[/
  Copyright 2006, 2007, 2008, 2009, 2010, 2012, 2013, 2015, 2016 John Maddock and Paul A. Bristow.
  Distributed under the Boost Software License, Version 1.0.
  (See accompanying file LICENSE_1_0.txt or copy at
  http://www.boost.org/LICENSE_1_0.txt).
]

