Package: ordinalNet 2.14

Michael Wurm

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>.

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

ordinalNet_2.14.tar.gz
ordinalNet_2.14.tar.gz(r-4.7-any)ordinalNet_2.14.tar.gz(r-4.6-any)
ordinalNet_2.14.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
ordinalNet/json (API)

# Install 'ordinalNet' in R:
install.packages('ordinalNet', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

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This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

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Last updated from:cbb6aeed84. Checks:4 OK. Indexed: yes.

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linux-devel-x86_64OK139
source / vignettesOK206
linux-release-x86_64OK130
wasm-releaseOK102

Exports:ordinalNetordinalNetCVordinalNetTune

Dependencies: