MGLM: Multivariate Response Generalized Linear Models

Provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data. Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported.

Version:	0.2.1
Depends:	R (≥ 3.0.0)
Imports:	methods, stats, parallel, stats4
Suggests:	ggplot2, plyr, reshape2, knitr, testthat (≥ 3.0.0)
Published:	2022-04-13
DOI:	10.32614/CRAN.package.MGLM
Author:	Yiwen Zhang and Hua Zhou
Maintainer:	Juhyun Kim <juhkim111 at ucla.edu>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
Citation:	MGLM citation info
CRAN checks:	MGLM results

Documentation:

Reference manual:	MGLM.html , MGLM.pdf
Vignettes:	MGLM Vignette (source, R code)

Downloads:

Package source:	MGLM_0.2.1.tar.gz
Windows binaries:	r-devel: MGLM_0.2.1.zip, r-release: MGLM_0.2.1.zip, r-oldrel: MGLM_0.2.1.zip
macOS binaries:	r-release (arm64): MGLM_0.2.1.tgz, r-oldrel (arm64): MGLM_0.2.1.tgz, r-release (x86_64): MGLM_0.2.1.tgz, r-oldrel (x86_64): MGLM_0.2.1.tgz
Old sources:	MGLM archive

Reverse dependencies:

Reverse suggests:

surveillance

Linking:

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