c(
person("Michael", "Wurm", email = "wurm@wisc.edu", role = c("aut", "cre")),
person("Paul", "Rathouz", email = "rathouz@biostat.wisc.edu", role = "aut"),
person("Bret", "Hanlon", email = "hanlon@stat.wisc.edu", role = "aut"))
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.
Michael Wurm [aut, cre],
Paul Rathouz [aut],
Bret Hanlon [aut]

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"))
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>.