std.mathspecial - D Programming Language
Description: D Programming Language
Keywords: D programming language

Menu

- Learn
- Documentation
  - Language Reference
  - Library Reference
  - Command-line Reference
  - Feature Overview
  - Articles
- Downloads
- Packages
- Community
  - Blog
  - Orgs using D
  - Twitter
  - Calendar
  - Forums
  - IRC
  - Community Discord
  - Wiki
  - GitHub
  - Issues
  - Get involved
  - Contributors
  - Foundation
  - Security Team
  - Donate
  - Sponsors
- Resources
  - Tour
  - Books
  - Tutorials
  - Tools
  - Editors
  - IDEs
  - run.dlang.io
  - Visual D
  - Acknowledgments
  - D Style
  - Glossary
  - Sitemap

Search
Report a bug
If you spot a problem with this page, click here to create a Bugzilla issue.
Improve this page
Quickly fork, edit online, and submit a pull request for this page. Requires a signed-in GitHub account. This works well for small changes. If you'd like to make larger changes you may want to consider using a local clone.

# std.mathspecial

Mathematical Special Functions
The technical term 'Special Functions' includes several families of transcendental functions, which have important applications in particular branches of mathematics and physics.
The gamma and related functions, and the error function are crucial for mathematical statistics. The Bessel and related functions arise in problems involving wave propagation (especially in optics). Other major categories of special functions include the elliptic integrals (related to the arc length of an ellipse), and the hypergeometric functions.

Status Many more functions will be added to this module. The naming convention for the distribution functions (gammaIncomplete, etc) is not yet finalized and will probably change.

License:
Boost License 1.0.
Authors:
Stephen L. Moshier (original C code). Conversion to D by Don Clugston

Source std/mathspecial.d

pure nothrow @nogc @safe real `gamma`(real `x`);
  The Gamma function, Γ(x)
  Γ(x) is a generalisation of the factorial function to real and complex numbers. Like x!, Γ(x+1) = x * Γ(x).
  Mathematically, if z.re > 0 then Γ(z) = ∫0∞ tz-1e-t dt

  Special Values
  x | Γ(x)
  --- | ---
  NAN | NAN
  ±0.0 | ±∞
  integer > 0 | (x-1)!
  integer < 0 | NAN
  +∞ | +∞
  -∞ | NAN

pure nothrow @nogc @safe real `logGamma`(real `x`);
  Natural logarithm of the gamma function, Γ(x)
  Returns the base e (2.718...) logarithm of the absolute value of the gamma function of the argument.
  For reals, logGamma is equivalent to log(fabs(gamma(x))).

  Special Values
  x | logGamma(x)
  --- | ---
  NAN | NAN
  integer <= 0 | +∞
  ±∞ | +∞

pure nothrow @nogc @safe real `sgnGamma`(real `x`);
  The sign of Γ(x).
  Returns -1 if Γ(x) < 0, +1 if Γ(x) > 0, NAN if sign is indeterminate.
  Note that this function can be used in conjunction with logGamma(x) to evaluate gamma for very large values of x.
pure nothrow @nogc @safe real `beta`(real `x`, real `y`);
  Beta function
  The beta function is defined as
  beta(x, y) = (Γ(x) * Γ(y)) / Γ(x + y)
pure nothrow @nogc @safe real `digamma`(real `x`);
  Digamma function
  The digamma function is the logarithmic derivative of the gamma function.
  digamma(x) = d/dx logGamma(x)
  See Also:
  logmdigamma, logmdigammaInverse.
pure nothrow @nogc @safe real `logmdigamma`(real `x`);
  Log Minus Digamma function
  logmdigamma(x) = log(x) - digamma(x)
  See Also:
  digamma, logmdigammaInverse.
pure nothrow @nogc @safe real `logmdigammaInverse`(real `x`);
  Inverse of the Log Minus Digamma function
  Given y, the function finds x such log(x) - digamma(x) = y.
  See Also:
  logmdigamma, digamma.
pure nothrow @nogc @safe real `betaIncomplete`(real `a`, real `b`, real `x`);
  Incomplete beta integral
  Returns regularized incomplete beta integral of the arguments, evaluated from zero to x. The regularized incomplete beta function is defined as
  betaIncomplete(a, b, x) = Γ(a + b) / ( Γ(a) Γ(b) ) * ∫0x ta-1(1-t)b-1 dt
  and is the same as the cumulative distribution function of the Beta distribution.
  The domain of definition is 0 <= x <= 1. In this implementation a and b are restricted to positive values. The integral from x to 1 may be obtained by the symmetry relation
  betaIncompleteCompl(a, b, x ) = betaIncomplete( b, a, 1-x )
  The integral is evaluated by a continued fraction expansion or, when b * x is small, by a power series.
pure nothrow @nogc @safe real `betaIncompleteInverse`(real `a`, real `b`, real `y`);
  Inverse of incomplete beta integral
  Given y, the function finds x such that
  betaIncomplete(a, b, x) == y
  Newton iterations or interval halving is used.
pure nothrow @nogc @safe real `gammaIncomplete`(real `a`, real `x`);
pure nothrow @nogc @safe real `gammaIncompleteCompl`(real `a`, real `x`);
  Incomplete gamma integral and its complement
  These functions are defined by
  gammaIncomplete = ( ∫0x e-t ta-1 dt )/ Γ(a)
  gammaIncompleteCompl(a,x) = 1 - gammaIncomplete(a,x) = (∫x∞ e-t ta-1 dt )/ Γ(a)
  In this implementation both arguments must be positive. The integral is evaluated by either a power series or continued fraction expansion, depending on the relative values of a and x.
pure nothrow @nogc @safe real `gammaIncompleteComplInverse`(real `a`, real `p`);
  Inverse of complemented incomplete gamma integral
  Given a and p, the function finds x such that
  gammaIncompleteCompl( a, x ) = p.
pure nothrow @nogc @safe real `erf`(real `x`);
  Error function
  The integral is
  erf(x) = 2/ √(π) ∫0x exp( - t2) dt
  The magnitude of x is limited to about 106.56 for IEEE 80-bit arithmetic; 1 or -1 is returned outside this range.
pure nothrow @nogc @safe real `erfc`(real `x`);
  Complementary error function
  erfc(x) = 1 - erf(x) = 2/ √(π) ∫x∞ exp( - t2) dt
  This function has high relative accuracy for values of x far from zero. (For values near zero, use erf(x)).
pure nothrow @nogc @safe real `normalDistribution`(real `x`);
  Standard normal distribution function.
  The normal (or Gaussian, or bell-shaped) distribution is defined as:
  normalDist(x) = 1/√(2π) ∫-∞x exp( - t2/2) dt = 0.5 + 0.5 * erf(x/sqrt(2)) = 0.5 * erfc(- x/sqrt(2))
  To maintain accuracy at values of x near 1.0, use normalDistribution(x) = 1.0 - normalDistribution(-x).

  References http://www.netlib.org/cephes/ldoubdoc.html, G. Marsaglia, "Evaluating the Normal Distribution", Journal of Statistical Software 11, (July 2004).

pure nothrow @nogc @safe real `normalDistributionInverse`(real `p`);
  Inverse of Standard normal distribution function
  Returns the argument, x, for which the area under the Normal probability density function (integrated from minus infinity to x) is equal to p.

  Note This function is only implemented to 80 bit precision.

Copyright © 1999-2023 by the D Language Foundation | Page generated by Ddoc on Sun Jan 1 02:20:51 2023