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 (2021) <doi:10.18637/jss.v099.i06>.

Version: 2.10
Imports: stats, graphics
Suggests: testthat (≥ 1.0.2), MASS (≥ 7.3-45), glmnet (≥ 2.0-5), penalized (≥ 0.9-50), VGAM (≥ 1.0-3), rms (≥ 5.1-0)
Published: 2021-09-05
Author: Michael Wurm [aut, cre], Paul Rathouz [aut], Bret Hanlon [aut]
Maintainer: Michael Wurm <wurm at uwalumni.com>
License: MIT + file LICENSE
NeedsCompilation: yes
Citation: ordinalNet citation info
CRAN checks: ordinalNet results

Documentation:

Reference manual: ordinalNet.pdf

Downloads:

Package source: ordinalNet_2.10.tar.gz
Windows binaries: r-devel: ordinalNet_2.10.zip, r-devel-UCRT: ordinalNet_2.10.zip, r-release: ordinalNet_2.10.zip, r-oldrel: ordinalNet_2.10.zip
macOS binaries: r-release (arm64): ordinalNet_2.10.tgz, r-release (x86_64): ordinalNet_2.10.tgz, r-oldrel: ordinalNet_2.10.tgz
Old sources: ordinalNet archive

Reverse dependencies:

Reverse imports: kosel, multiMarker

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