engines checklist

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()

• 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() (Archer and Williams, 2012)
• continuation ratio (stopping ratio) ordinal regression using regularization path computation via glmpathcr::glmpathcr() (Archer and Williams, 2012)
• generalized monotone incremental forward stagewise regularized ordinal regression via ordinalgmifs::ordinalgmifs() (Archer, Hou, Zhou, Ferber, Layne, and Gentry, 2014; Gentry, Jackson-Cook, Lyon, and Archer, 2015)
• Bayesian LASSO ordinal regression via ordinalbayes::ordinalbayes() (Zhang and Archer, 2021)
• cumulative link ordinal regression via ordinal::clm() (Christensen, 2023)
• nonlinear and generalized linear ordinal regression via gnlm::nordr() and gnlm::ordglm() (CRAN; GitHub)
• constrained ordinal regression via crov::mdcp() (Espinosa and Hennig, 2019; CRAN)

gen_additive_mod()

• generalized additive ordinal regression models of cumulative link, adjacent categories, continuation ratio, and stopping ratio families via VGAM::vgam() (Yee, 2015)

decision_tree()

• ordinal classification trees via rpartScore::rpartScore() (Galimberti, Soffritti, and Di Maso, 2012)

rand_forest()

• latent variable ordinal forests via ordinalForest::ordfor() (Hornung, 2020)
• conditional probability ordered forests via orf::orf() (Lechner and Okasa, 2025)
• ordered correlation forests via ocf::ocf() (Di Francesco, 2025; CRAN; GitHub)

uncertain

• tree-based varying coefficient ordinal mixed models via vcrpart::tvcm() (Bürgin and Ritschard, 2017; CRAN)

Start with the unchecked engine entries in the issue and read the linked tidymodels contribution guidelines and parsnip engine checklist. Choose one engine and confirm its package and model scope before beginning. Done means the selected engine is integrated, documented, tested, and its checklist entry is updated in a pull request.