Package: ordinalNet
Type: Package
Title: Penalized Ordinal Regression
Version: 2.12
Authors@R: c(
    person("Michael", "Wurm", email = "wurm@uwalumni.com", role = c("aut", "cre")),
    person("Paul", "Rathouz", email = "rathouz@biostat.wisc.edu", role = "aut"),
    person("Bret", "Hanlon", email = "hanlon@stat.wisc.edu", role = "aut"))	
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) 
    <doi:10.18637/jss.v099.i06>.
License: MIT + file LICENSE
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)
RoxygenNote: 7.1.1
NeedsCompilation: yes
Packaged: 2022-03-22 02:50:08 UTC; mike
Author: Michael Wurm [aut, cre],
  Paul Rathouz [aut],
  Bret Hanlon [aut]
Maintainer: Michael Wurm <wurm@uwalumni.com>
Repository: CRAN
Date/Publication: 2022-03-22 08:10:02 UTC
Built: R 4.1.3; ; 2023-04-17 13:30:02 UTC; windows
