## ordinalNet: Penalized Ordinal Regression

Fits ordinal regression models with elastic net penalty.
Supported model families include cumulative probability, stopping ratio,
continuation ratio, and adjacent category. These families are a subset of
vector glm's which belong to a model class we call the elementwise link
multinomial-ordinal (ELMO) class. Each family in this class links a vector
of covariates to a vector of class probabilities. Each of these families
has a parallel form, which is appropriate for ordinal response data, as
well as a nonparallel form that is appropriate for an unordered categorical
response, or as a more flexible model for ordinal data. The parallel model
has a single set of coefficients, whereas the nonparallel model has a set of
coefficients for each response category except the baseline category. It is
also possible to fit a model with both parallel and nonparallel terms, which
we call the semi-parallel model. The semi-parallel model has the flexibility
of the nonparallel model, but the elastic net penalty shrinks it toward the
parallel model. For details, refer to Wurm, Hanlon, and Rathouz (2017)
<arXiv:1706.05003>.

Version: |
2.8 |

Imports: |
stats, graphics |

Suggests: |
testthat (≥ 1.0.2), MASS (≥ 7.3-45), glmnet (≥ 2.0-5), penalized (≥ 0.9-50), glmnetcr (≥ 1.0.3), VGAM (≥ 1.0-3), rms (≥ 5.1-0) |

Published: |
2020-05-25 |

Author: |
Michael Wurm [aut, cre],
Paul Rathouz [aut],
Bret Hanlon [aut] |

Maintainer: |
Michael Wurm <wurm at uwalumni.com> |

License: |
MIT + file LICENSE |

NeedsCompilation: |
yes |

CRAN checks: |
ordinalNet results |

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