You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This issue is a checklist of engines under consideration, including those already implemented (for illustration), taken from an older README.
Ideally each new engine will be requested by at least two users/developers, one of whom will adopt it (taking responsibility to incorporate it in a new branch and submit a PR when it is ready).
This package and the broader ecosystem benefit from feedback by new developers as well as users; please feel free to begin working on a new engine even if you're not familiar with the process!
cumulative link (cumulative logit) ordinal regression via MASS::polr()
generalized linear ordinal regression models of cumulative link, adjacent categories, continuation ratio, and stopping ratio families via VGAM::vglm() (Yee, 2015)
regularized elastic net ordinal regression models of cumulative link, adjacent categories, continuation ratio, and stopping ratio families via ordinalNet::ordinalNet() (Wurm, Hanlon, and Rathouz, 2021)
regularized cumulative probability (cumulative logit) ordinal regression via rms::lrm() and rms::orm() (Harrell, 2015)
continuation ratio (stopping ratio) ordinal regression using elastic net regularization via glmnetcr::glmnetcr() and using regularization path computation via glmpathcr::glmpathcr() (Archer and Williams, 2012)
generalized additive ordinal regression models of cumulative link, adjacent categories, continuation ratio, and stopping ratio families via VGAM::vgam() (Yee, 2015)
This issue is a checklist of engines under consideration, including those already implemented (for illustration), taken from an older README.
Ideally each new engine will be requested by at least two users/developers, one of whom will adopt it (taking responsibility to incorporate it in a new branch and submit a PR when it is ready).
This package and the broader ecosystem benefit from feedback by new developers as well as users; please feel free to begin working on a new engine even if you're not familiar with the process!
If you do adopt an engine, please follow the guidelines for contributing to tidymodels and use the checklists for adding and documenting a new engine.
ordinal_reg()MASS::polr()VGAM::vglm()(Yee, 2015)ordinalNet::ordinalNet()(Wurm, Hanlon, and Rathouz, 2021)rms::lrm()andrms::orm()(Harrell, 2015)glmnetcr::glmnetcr()and using regularization path computation viaglmpathcr::glmpathcr()(Archer and Williams, 2012)ordinalgmifs::ordinalgmifs()(Archer, Hou, Zhou, Ferber, Layne, and Gentry, 2014; Gentry, Jackson-Cook, Lyon, and Archer, 2015)ordinalbayes::ordinalbayes()(Zhang and Archer, 2021)ordinal::clm()(Christensen, 2023)gnlm::nordr()andgnlm::ordglm()(CRAN; GitHub)crov::mdcp()(Espinosa and Hennig, 2019; CRAN)gen_additive_mod()VGAM::vgam()(Yee, 2015)decision_tree()rpartScore::rpartScore()(Galimberti, Soffritti, and Di Maso, 2012)rand_forest()ordinalForest::ordfor()(Hornung, 2020)orf::orf()(Lechner and Okasa, 2025)ocf::ocf()(Di Francesco, 2025; CRAN; GitHub)uncertain
vcrpart::tvcm()(Bürgin and Ritschard, 2017; CRAN)