mvdigamma

A special mathematical function related to the gamma function,
 generalized for multivariate distributions.
 The multivariate digamma function is the derivative of the log of the
 multivariate gamma function; for $p = 1$ it is the same as the
 univariate digamma function.
$$\psi_{p}(a)=\sum _{i=1}^{p}\psi(a+(1-i)/2)
 $$
 where $\psi$ is the univariate digamma function (the
 derivative of the log-gamma function).

Sampling from the Cholesky factorization of a Wishart random
variable, sampling from the inverse Wishart distribution, sampling from
the Cholesky factorization of an inverse Wishart random variable, sampling
from the pseudo Wishart distribution, sampling from the generalized
inverse Wishart distribution, computing densities for the Wishart
and inverse Wishart distributions, and computing the multivariate gamma
and digamma functions. Provides a header file so the C functions can be
called directly from other programs.

Geoffrey Thompson

CholWishart

Cholesky Decomposition of the Wishart Distribution

R Core Team 

mvdigamma function

<dl><dt>x</dt>
<dd>non-negative numeric vector, matrix, or array</dd>
<dt>p</dt>
<dd>positive integer, dimension of a square matrix</dd></dl>

Arguments

Multivariate Digamma Function — mvdigamma

<dl>

<dt>x</dt>
<dd>non-negative numeric vector, matrix, or array</dd>


<dt>p</dt>
<dd>positive integer, dimension of a square matrix</dd>

</dl>

mvdigamma: Multivariate Digamma Function

Description

Usage

Value

Arguments

References

See Also

Examples