In statistical modeling, there is a wide variety of regression models for categorical dependent variables (nominal or ordinal data); yet, there is no software embracing all these models together in a uniform and generalized format. Following the methodology proposed by Peyhardi, Trottier, and Guédon (2015)
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("ylleonv/GLMcat")
We introduce and illustrate GLMcat, the R package we developed to estimate generalized linear models implemented under the unified specification $(r, F, Z)$, where $r$ represents the ratio of probabilities (reference, cumulative, adjacent or sequential), $F$ the cumulative cdf function for the linkage, and $Z$ the design matrix. We present the properties of the four families of models, which must be investigated when selecting the components $r$, $F$, and $Z$. The functions are user-friendly and fairly intuitive; offering the possibility to choose from a large range of models through a combination $(r, F, Z)$.