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ordinalNet  

Penalized Ordinal Regression
View on CRAN: Click here


Download and install ordinalNet package within the R console
Install from CRAN:
install.packages("ordinalNet")

Install from Github:
library("remotes")
install_github("cran/ordinalNet")

Install by package version:
library("remotes")
install_version("ordinalNet", "2.13")



Attach the package and use:
library("ordinalNet")
Maintained by
Michael Wurm
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2015-11-22
Latest Update: 2025-05-15
Description:
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) .
How to cite:
Michael Wurm (2015). ordinalNet: Penalized Ordinal Regression. R package version 2.13, https://cran.r-project.org/web/packages/ordinalNet. Accessed 05 Aug. 2026.
Previous versions and publish date:
(2026-07-09 06:37), 1.1 (2015-11-22 11:48), 1.2 (2015-11-25 08:54), 1.3 (2015-11-30 23:57), 1.4 (2015-12-10 09:52), 1.5 (2016-07-27 10:58), 2.0 (2017-05-08 23:17), 2.1 (2017-08-19 06:09), 2.2 (2017-08-22 19:29), 2.3 (2017-10-03 10:49), 2.4 (2017-12-05 11:56), 2.5 (2018-09-10 22:20), 2.6 (2019-02-21 06:40), 2.7 (2020-01-10 16:00), 2.8 (2020-05-25 22:00), 2.9 (2020-07-21 08:30), 2.10 (2021-09-05 06:30), 2.11 (2022-02-16 11:20), 2.12 (2022-03-22 09:10), 2.13 (2025-05-15 20:00)
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