3.13 Combinatorial Functions
real beta(real alpha, real beta)
Return the beta function applied to alpha and beta. The beta function,
\(\text{B}(\alpha,\beta)\), computes the normalizing constant for the beta
distribution, and is defined for \(\alpha > 0\) and \(\beta > 0\). See section
appendix for definition of \(\text{B}(\alpha, \beta)\).
R beta(T1 x, T2 y)
Vectorized implementation of the beta function
real inc_beta(real alpha, real beta, real x)
Return the regularized incomplete beta function up to x applied to alpha and beta.
See section appendix for a definition.
real lbeta(real alpha, real beta)
Return the natural logarithm of the beta function applied to alpha and
beta. The beta function, \(\text{B}(\alpha,\beta)\), computes the
normalizing constant for the beta distribution, and is defined for
\(\alpha > 0\) and \(\beta > 0\). \[ \text{lbeta}(\alpha,\beta) = \log
\Gamma(a) + \log \Gamma(b) - \log \Gamma(a+b) \] See section
appendix for definition of \(\text{B}(\alpha, \beta)\).
R lbeta(T1 x, T2 y)
Vectorized implementation of the lbeta function
R tgamma(T x)
gamma function applied to x. The gamma function is the generalization
of the factorial function to continuous variables, defined so that
\(\Gamma(n+1) = n!\). See for a full definition of \(\Gamma(x)\). The
function is defined for positive numbers and non-integral negative
numbers,
R lgamma(T x)
natural logarithm of the gamma function applied to x,
R digamma(T x)
digamma function applied to x. The digamma function is the derivative
of the natural logarithm of the Gamma function. The function is
defined for positive numbers and non-integral negative numbers
R trigamma(T x)
trigamma function applied to x. The trigamma function is the second
derivative of the natural logarithm of the Gamma function
real lmgamma(int n, real x)
Return the natural logarithm of the multivariate gamma function
\(\Gamma_n\) with n dimensions applied to x. \[ \text{lmgamma}(n,x) =
\begin{cases} \frac{n(n-1)}{4} \log \pi + \sum_{j=1}^n \log
\Gamma\left(x + \frac{1 - j}{2}\right) & \text{if } x\not\in
\{\dots,-3,-2,-1,0\}\\ \textrm{error} & \text{otherwise} \end{cases}
\]
R lmgamma(T1 x, T2 y)
Vectorized implementation of the lmgamma function
real gamma_p(real a, real z)
Return the normalized lower incomplete gamma function of a and z
defined for positive a and nonnegative z. \[ \mathrm{gamma\_p}(a,z) =
\begin{cases} \frac{1}{\Gamma(a)}\int_0^zt^{a-1}e^{-t}dt & \text{if }
a > 0, z \geq 0 \\ \textrm{error} & \text{otherwise} \end{cases} \]
R gamma_p(T1 x, T2 y)
Vectorized implementation of the gamma_p function
real gamma_q(real a, real z)
Return the normalized upper incomplete gamma function of a and z
defined for positive a and nonnegative z. \[ \mathrm{gamma\_q}(a,z) =
\begin{cases} \frac{1}{\Gamma(a)}\int_z^\infty t^{a-1}e^{-t}dt &
\text{if } a > 0, z \geq 0 \\[6pt] \textrm{error} & \text{otherwise}
\end{cases} \]
R gamma_q(T1 x, T2 y)
Vectorized implementation of the gamma_q function
real binomial_coefficient_log(real x, real y)
Warning: This function is deprecated and should be replaced with
lchoose. Return the natural logarithm of the binomial coefficient of
x and y. For non-negative integer inputs, the binomial coefficient
function is written as \(\binom{x}{y}\) and pronounced “x choose y.”
This function generalizes to real numbers using the gamma function.
For \(0 \leq y \leq x\), \[ \mathrm{binomial\_coefficient\_log}(x,y) =
\log\Gamma(x+1) - \log\Gamma(y+1) - \log\Gamma(x-y+1). \]
R binomial_coefficient_log(T1 x, T2 y)
Vectorized implementation of the binomial_coefficient_log function
int choose(int x, int y)
Return the binomial coefficient of x and y. For non-negative integer
inputs, the binomial coefficient function is written as \(\binom{x}{y}\)
and pronounced “x choose y.” In its the antilog of the lchoose
function but returns an integer rather than a real number with no
non-zero decimal places. For \(0 \leq y \leq x\), the binomial
coefficient function can be defined via the factorial function \[
\text{choose}(x,y) = \frac{x!}{\left(y!\right)\left(x - y\right)!}. \]
R choose(T1 x, T2 y)
Vectorized implementation of the choose function
real bessel_first_kind(int v, real x)
Return the Bessel function of the first kind with order v applied to
x. \[ \mathrm{bessel\_first\_kind}(v,x) = J_v(x), \] where \[
J_v(x)=\left(\frac{1}{2}x\right)^v \sum_{k=0}^\infty
\frac{\left(-\frac{1}{4}x^2\right)^k}{k!\, \Gamma(v+k+1)} \]
R bessel_first_kind(T1 x, T2 y)
Vectorized implementation of the bessel_first_kind function
real bessel_second_kind(int v, real x)
Return the Bessel function of the second kind with order v applied to
x defined for positive x and v. For \(x,v > 0\), \[
\mathrm{bessel\_second\_kind}(v,x) = \begin{cases} Y_v(x) & \text{if }
x > 0 \\ \textrm{error} & \text{otherwise} \end{cases} \] where \[
Y_v(x)=\frac{J_v(x)\cos(v\pi)-J_{-v}(x)}{\sin(v\pi)} \]
R bessel_second_kind(T1 x, T2 y)
Vectorized implementation of the bessel_second_kind function
real modified_bessel_first_kind(int v, real z)
Return the modified Bessel function of the first kind with order v
applied to z defined for all z and v. \[
\mathrm{modified\_bessel\_first\_kind}(v,z) = I_v(z) \] where \[
{I_v}(z) = \left(\frac{1}{2}z\right)^v\sum_{k=0}^\infty
\frac{\left(\frac{1}{4}z^2\right)^k}{k!\Gamma(v+k+1)} \]
R modified_bessel_first_kind(T1 x, T2 y)
Vectorized implementation of the modified_bessel_first_kind function
real modified_bessel_second_kind(int v, real z)
Return the modified Bessel function of the second kind with order v
applied to z defined for positive z and v. \[
\mathrm{modified\_bessel\_second\_kind}(v,z) = \begin{cases} K_v(z) &
\text{if } z > 0 \\ \textrm{error} & \text{if } z \leq 0 \end{cases}
\] where \[ {K_v}(z) = \frac{\pi}{2}\cdot\frac{I_{-v}(z) -
I_{v}(z)}{\sin(v\pi)} \]
R modified_bessel_second_kind(T1 x, T2 y)
Vectorized implementation of the modified_bessel_second_kind function
real falling_factorial(real x, real n)
Return the falling factorial of x with power n defined for positive x
and real n. \[ \mathrm{falling\_factorial}(x,n) = \begin{cases} (x)_n
& \text{if } x > 0 \\ \textrm{error} & \text{if } x \leq 0 \end{cases}
\] where \[ (x)_n=\frac{\Gamma(x+1)}{\Gamma(x-n+1)} \]
R falling_factorial(T1 x, T2 y)
Vectorized implementation of the falling_factorial function
real lchoose(real x, real y)
Return the natural logarithm of the generalized binomial coefficient
of x and y. For non-negative integer inputs, the binomial coefficient
function is written as \(\binom{x}{y}\) and pronounced “x choose y.”
This function generalizes to real numbers using the gamma function.
For \(0 \leq y \leq x\), \[ \mathrm{binomial\_coefficient\_log}(x,y) =
\log\Gamma(x+1) - \log\Gamma(y+1) - \log\Gamma(x-y+1). \]
real log_falling_factorial(real x, real n)
Return the log of the falling factorial of x with power n defined for
positive x and real n. \[ \mathrm{log\_falling\_factorial}(x,n) =
\begin{cases} \log (x)_n & \text{if } x > 0 \\ \textrm{error} &
\text{if } x \leq 0 \end{cases} \]
real rising_factorial(real x, int n)
Return the rising factorial of x with power n defined for positive x
and integer n. \[ \mathrm{rising\_factorial}(x,n) = \begin{cases} x^{(n)}
& \text{if } x > 0 \\ \textrm{error} & \text{if } x \leq 0 \end{cases}
\] where \[ x^{(n)}=\frac{\Gamma(x+n)}{\Gamma(x)} \]
R rising_factorial(T1 x, T2 y)
Vectorized implementation of the rising_factorial function
real log_rising_factorial(real x, real n)
Return the log of the rising factorial of x with power n defined for
positive x and real n. \[ \mathrm{log\_rising\_factorial}(x,n) =
\begin{cases} \log x^{(n)} & \text{if } x > 0 \\ \textrm{error} &
\text{if } x \leq 0 \end{cases} \]
R log_rising_factorial(T1 x, T2 y)
Vectorized implementation of the log_rising_factorial function