mvdigamma: Multivariate Digamma Function

In CholWishart: Cholesky Decomposition of the Wishart Distribution

Multivariate Digamma Function

Description

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).

Usage

mvdigamma(x, p)

Arguments

x	non-negative numeric vector, matrix, or array
p	positive integer, dimension of a square matrix

Value

vector of values of multivariate digamma function.

References

A. K. Gupta and D. K. Nagar 1999. Matrix variate distributions. Chapman and Hall.

Multivariate gamma function. In Wikipedia, The Free Encyclopedia,from https://en.wikipedia.org/w/index.php?title=Multivariate_gamma_function

See Also

gamma, lgamma, digamma, and mvgamma

Examples

digamma(1:10)
mvdigamma(1:10, 1L)

CholWishart documentation built on Oct. 1, 2024, 1:06 a.m.