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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