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hgm (version 1.10)

Holonomic Gradient Method and Gradient Descent

Description

The holonomic gradient method (HGM, hgm) gives a way to evaluate normalization constants of unnormalized probability distributions by utilizing holonomic systems of differential or difference equations. The holonomic gradient descent (HGD, hgd) gives a method to find maximal likelihood estimates by utilizing the HGM.

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Version

Install

install.packages('hgm')

Monthly Downloads

192

Version

1.10

License

GPL-2

Maintainer

Nobuki Takayama

Last Published

March 27th, 2015

Functions in hgm (1.10)

hgm-package

HGM
hgm.ncso3

The function hgm.ncso3 evaluates the normalization constant for the Fisher distribution on SO(3).
hgm.pwishart

The function hgm.pwishart evaluates the cumulative distribution function of random wishart matrices.
hgm.ncorthant

The function hgm.ncorthant evaluates the orthant probability.
hgm.Rhgm.demo1

The function hgm.Rhgm.demo1 performs a demonstration of the function hgm.Rhgm.
hgm.ncBingham

The function hgm.ncBingham performs the holonomic gradient method (HGM) for Bingham distributions.
hgm.Rhgm

The function hgm.Rhgm performs the holonomic gradient method (HGM) for a given Pfaffian system and an initial value vector.