hgm: Holonomic Gradient Method and Gradient Descent

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.

Version: 1.23
Depends: R (≥ 2.6.0), deSolve
Published: 2023-01-31
DOI: 10.32614/CRAN.package.hgm
Author: Nobuki Takayama, Tamio Koyama, Tomonari Sei, Hiromasa Nakayama, Kenta Nishiyama
Maintainer: Nobuki Takayama <takayama at math.kobe-u.ac.jp>
License: GPL-2
URL: http://www.openxm.org
NeedsCompilation: yes
CRAN checks: hgm results


Reference manual: hgm.pdf


Package source: hgm_1.23.tar.gz
Windows binaries: r-devel: hgm_1.23.zip, r-release: hgm_1.23.zip, r-oldrel: hgm_1.23.zip
macOS binaries: r-release (arm64): hgm_1.23.tgz, r-oldrel (arm64): hgm_1.23.tgz, r-release (x86_64): hgm_1.23.tgz, r-oldrel (x86_64): hgm_1.23.tgz
Old sources: hgm archive


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