The R Journal: article published in 2018, volume 10:1

MGLM: An R Package for Multivariate Categorical Data Analysis
Juhyun Kim, Yiwen Zhang, Joshua Day and Hua Zhou , The R Journal (2018) 10:1, pages 73-90.

Abstract Data with multiple responses is ubiquitous in modern applications. However, few tools are available for regression analysis of multivariate counts. The most popular multinomial-logit model has a very restrictive mean-variance structure, limiting its applicability to many data sets. This article introduces an R package MGLM, short for multivariate response generalized linear models, that expands the current tools for regression analysis of polytomous data. Distribution fitting, random number generation, regression, and sparse regression are treated in a unifying framework. The algorithm, usage, and implementation details are discussed.

Received: 2017-05-09; online 2018-05-16, supplementary material, (1.7 KiB)
CRAN packages: MGLM, VGAM, glmnet, dirmult, parallel, isoform, glmc
CRAN Task Views implied by cited CRAN packages: Distributions, Survival, Econometrics, Environmetrics, ExtremeValue, MachineLearning, Multivariate, Psychometrics, SocialSciences

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-015,
  author = {Juhyun Kim and Yiwen Zhang and Joshua Day and Hua Zhou},
  title = {{MGLM: An R Package for Multivariate Categorical Data
          Analysis}},
  year = {2018},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2018-015},
  url = {https://doi.org/10.32614/RJ-2018-015},
  pages = {73--90},
  volume = {10},
  number = {1}
}