I was wondering if someone would know an R package that would allow me to fit an Ordinal Logistic regression with a LASSO regularization or, alternatively, a Beta regression still with the LASSO? And if you also know of a nice tutorial to help me code that in R (with appropriate cross-validation), that would be even better!

Some context: My response variable is a satisfaction score between 0 and 10 (actually, values lie between 2 and 10) so I can model it with a Beta regression or I can convert its values into ranked categories. My interest is to identify important variables explaining this score but as I have too many potential explanatory variables (p = 12) compared to my sample size (n = 105), I need to use a penalized regression method for model selection, hence my interest in the LASSO.

CodePudding user response：

The ordinalNet package does this. There's a paper with example here: https://www.jstatsoft.org/article/download/v099i06/1440

Also the glmnetcr package: https://cran.r-project.org/web/packages/glmnetcr/vignettes/glmnetcr.pdf