{ordered}: a #tidymodels extension for ordinal classification / ordinal regression models is now on CRAN!
I loved digging into and tinkering with the tidymodels infrastructure to get this initial version up and running. If you want {ordered} to accommodate your favorite model-fitting engine, please open an issue and be ready to chat through it. : )
CRAN link:
https://cran.r-project.org/web/packages/ordered/index.html
Source code:
https://github.com/corybrunson/ordered

ordered: 'parsnip' Engines and Wrappers for Ordinal Classification Models
Bindings, methods, and tuners for using ordinal classification models with the 'parsnip' and 'dials' packages. These include the regularized elastic net ordinal regression of Wurm, Hanlon, and Rathouz (2021) <<a href="https://doi.org/10.18637%2Fjss.v099.i06" target="_top">doi:10.18637/jss.v099.i06</a>> in 'ordinalNet', the ordinal classification trees of Galimberti, Soffritti, and Di Maso (2012) <<a href="https://doi.org/10.18637%2Fjss.v047.i10" target="_top">doi:10.18637/jss.v047.i10</a>> in 'rpartScore', and the latent variable ordinal forests of Hornung (2020) <<a href="https://doi.org/10.1007%2Fs00357-018-9302-x" target="_top">doi:10.1007/s00357-018-9302-x</a>> in 'ordinalForest'.





. The large context window (1M tokens) and the ability to set the temperature to 0, meaning NO CREATIVITY, make this LLM a very good tool for RAG when communicating with your own materials. For example, I recently had a small question about the applicability of a ridge regression model that I trained in