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 , 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7286576 | PMC |
| http://dx.doi.org/10.32614/rj-2018-015 | DOI Listing |
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