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								<div class="section">
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								<div class="titlepage"><div><div><h2 class="title" style="clear: both">
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								<a name="math_toolkit.credits"></a><a class="link" href="credits.html" title="Credits and Acknowledgements">Credits and Acknowledgements</a>
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								</h2></div></div></div>
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								<p>
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								      Hubert Holin started the Boost.Math library. The Quaternions, Octonions, inverse
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								      hyperbolic functions, and the sinus cardinal functions are his.
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								    </p>
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								<p>
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								      Daryle Walker wrote the integer gcd and lcm functions.
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								    </p>
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								<p>
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								      John Maddock started the special functions, the beta, gamma, erf, polynomial,
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								      and factorial functions are his, as is the "Toolkit" section, and
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								      many of the statistical distributions.
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								    </p>
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								<p>
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								      Paul A. Bristow threw down the challenge in <a href="http://www2.open-std.org/JTC1/SC22/WG21/docs/papers/2004/n1668.pdf" target="_top">A
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								      Proposal to add Mathematical Functions for Statistics to the C++ Standard Library</a>
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								      to add the key math functions, especially those essential for statistics. After
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								      JM accepted and solved the difficult problems, not only numerically, but in
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								      full C++ template style, PAB implemented a few of the statistical distributions.
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								      PAB also tirelessly proof-read everything that JM threw at him (so that all
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								      remaining editorial mistakes are his fault).
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								    </p>
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								<p>
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								      Xiaogang Zhang worked on the Bessel functions and elliptic integrals for his
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								      Google Summer of Code project 2006.
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								    </p>
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								<p>
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								      Bruno Lalande submitted the "compile time power of a runtime base"
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								      code.
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								    </p>
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								<p>
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								      Johan Råde wrote the optimised floating-point classification and manipulation
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								      code, and nonfinite facets to permit C99 output of infinities and NaNs. (nonfinite
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								      facets were not added until Boost 1.47 but had been in use with Boost.Spirit).
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								      This library was based on a suggestion from Robert Ramey, author of Boost.Serialization.
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								      Paul A. Bristow expressed the need for better handling of <a href="http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2006/n2022.pdf" target="_top">Input
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								      & Output of NaN and infinity for the C++ Standard Library</a> and suggested
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								      following the C99 format.
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								    </p>
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								<p>
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								      Antony Polukhin improved lexical cast avoiding stringstream so that it was
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								      no longer necessary to use a globale C99 facet to handle nonfinites.
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								    </p>
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								<p>
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								      Håkan Ardö, Boris Gubenko, John Maddock, Markus Schöpflin and Olivier Verdier
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								      tested the floating-point library and Martin Bonner, Peter Dimov and John Maddock
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								      provided valuable advice.
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								    </p>
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								<p>
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								      Gautam Sewani coded the logistic distribution as part of a Google Summer of
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								      Code project 2008.
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								    </p>
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								<p>
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								      M. A. (Thijs) van den Berg coded the Laplace distribution. (Thijs has also
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								      threatened to implement some multivariate distributions).
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								    </p>
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								<p>
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								      Thomas Mang requested the inverse gamma in chi squared distributions for Bayesian
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								      applications and helped in their implementation, and provided a nice example
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								      of their use.
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								    </p>
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								<p>
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								      Professor Nico Temme for advice on the inverse incomplete beta function.
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								    </p>
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								<p>
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								      <a href="http://www.shoup.net" target="_top">Victor Shoup for NTL</a>, without which
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								      it would have much more difficult to produce high accuracy constants, and especially
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								      the tables of accurate values for testing.
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								    </p>
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								<p>
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								      We are grateful to Joel Guzman for helping us stress-test his <a href="http://www.boost.org/tools/quickbook/index.htm" target="_top">Boost.Quickbook</a>
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								      program used to generate the html and pdf versions of this document, adding
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								      several new features en route.
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								    </p>
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								<p>
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								      Plots of the functions and distributions were prepared in <a href="http://www.w3.org/" target="_top">W3C</a>
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								      standard <a href="http://www.svg.org/" target="_top">Scalable Vector Graphic (SVG)</a>
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								      format using a program created by Jacob Voytko during a <a href="http://code.google.com/soc/2007/" target="_top">Google
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								      Summer of Code (2007)</a>. From 2012, the latest versions of all Internet
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								      Browsers have support for rendering SVG (with varying quality). Older versions,
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								      especially (Microsoft Internet Explorer (before IE 9) lack native SVG support
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								      but can be made to work with <a href="http://www.adobe.com/svg/viewer/install/" target="_top">Adobe's
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								      free SVG viewer</a> plugin). The SVG files can be converted to JPEG or
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								      PNG using <a href="http://www.inkscape.org/" target="_top">Inkscape</a>.
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								    </p>
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								<p>
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								      We are also indebted to Matthias Schabel for managing the formal Boost-review
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								      of this library, and to all the reviewers - including Guillaume Melquiond,
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								      Arnaldur Gylfason, John Phillips, Stephan Tolksdorf and Jeff Garland - for
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								      their many helpful comments.
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								    </p>
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								<p>
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								      Thanks to Mark Coleman and Georgi Boshnakov for spot test values from <a href="http://www.wolfram.com/products/mathematica/index.html" target="_top">Wolfram Mathematica</a>,
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								      and of course, to Eric Weisstein for nurturing <a href="http://mathworld.wolfram.com" target="_top">Wolfram
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								      MathWorld</a>, an invaluable resource.
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								    </p>
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								<p>
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								      The Skew-normal distribution and Owen's t function were written by Benjamin
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								      Sobotta.
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								    </p>
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								<p>
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								      We thank Thomas Mang for persuading us to allow t distributions to have infinite
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								      degrees of freedom and contributing to some long discussions about how to improve
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								      accuracy for large non-centrality and/or large degrees of freedom.
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								    </p>
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								<p>
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								      Christopher Kormanyos wrote the e_float multiprecision library <a href="http://portal.acm.org/citation.cfm?id=1916469" target="_top">TOMS
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								      Algorithm 910: A Portable C++ Multiple-Precision System for Special-Function
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								      Calculations</a> which formed the basis for the Boost.Multiprecision library
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								      which now can be used to allow most functions and distributions to be computed
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								      up to a precision of the users' choice, no longer restricted to built-in floating-point
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								      types like double. (And thanks to Topher Cooper for bring Christopher's e_float
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								      to our attention).
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								    </p>
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								<p>
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								      Christopher Kormanyos wrote some examples for using <a href="../../../../../libs/multiprecision/doc/html/index.html" target="_top">Boost.Multiprecision</a>,
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								      and added methods for finding zeros of Bessel Functions.
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								    </p>
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								<p>
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								      Marco Guazzone provided the hyper-geometric distribution.
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								    </p>
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								<p>
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								      Rocco Romeo has found numerous small bugs and generally stress tested the special
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								      functions code to near destruction!
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								    </p>
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								<p>
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								      Jeremy William Murphy added polynomial arithmetic tools.
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								    </p>
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								<p>
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								      Thomas Luu provided improvements to the quantile of the non-central chi squared
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								      distribution quantile. and his thesis * <a href="http://discovery.ucl.ac.uk/1482128/" target="_top">Fast
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								      and accurate parallel computation of quantile functions for random number generation,
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								      2016</a>.
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								    </p>
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								<p>
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								      and his paper
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								    </p>
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								<p>
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								      Luu, Thomas; (2015), Efficient and Accurate Parallel Inversion of the Gamma
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								      Distribution, SIAM Journal on Scientific Computing , 37 (1) C122 - C141, <a href="http://dx.doi.org/10.1137/14095875X" target="_top">http://dx.doi.org/10.1137/14095875X</a>.
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								    </p>
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								<p>
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								      These also promise to help improve algorithms for computation of quantile of
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								      several disitributions, especially for parallel computation using GPUs.
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								    </p>
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								</div>
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								<table xmlns:rev="http://www.cs.rpi.edu/~gregod/boost/tools/doc/revision" width="100%"><tr>
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								<td align="left"></td>
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								<td align="right"><div class="copyright-footer">Copyright © 2006-2010, 2012-2014 Nikhar Agrawal,
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								      Anton Bikineev, Paul A. Bristow, Marco Guazzone, Christopher Kormanyos, Hubert
							 | 
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								      Holin, Bruno Lalande, John Maddock, Jeremy Murphy, Johan Råde, Gautam Sewani,
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								      Benjamin Sobotta, Thijs van den Berg, Daryle Walker and Xiaogang Zhang<p>
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								        Distributed under the Boost Software License, Version 1.0. (See accompanying
							 | 
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								        file LICENSE_1_0.txt or copy at <a href="http://www.boost.org/LICENSE_1_0.txt" target="_top">http://www.boost.org/LICENSE_1_0.txt</a>)
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								      </p>
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