Imports: Rcpp (>= 0.11.2), combinat, ggplot2, nnet

Hornung R. (2019) Ordinal Forests. Journal of Classification,

Imports: Rcpp (>= 0.11.2), combinat, nnet, verification
NOTE: Starting with package version 2.4, it is also possible to obtain class probability
predictions in addition to the class point predictions, where the variable importance values
are also obtained based on the class probabilities.
Hornung R. (2020) Ordinal Forests. Journal of Classification 37, 4–17.

possible to rank the covariates with respect to their importance in the prediction of the
predictions in addition to the class point predictions. Moreover, the variable importance values
can also be based on the class probability predictions. Preliminary results indicate that
this might lead to a better discrimination between influential and non-influential covariates.

Title: Co-Clustering of Ordinal Data via Latent Continuous Random
Author: Marco Corneli, Charles Bouveyron and Pierre Latouche
Description: It implements functions for simulation and estimation of the ordinal latent block model (OLBM), as described in Corneli, Bouveyron and Latouche (2019).

Authors@R: person("Mike", "Wurm", email = "wurm@wisc.edu",
Description: Fits ordinal regression models with elastic net penalty.
Supported models include cumulative logit, probit, cauchit, and complementary log-log.
The algorithm uses Fisher Scoring with Coordinate Descent updates.
Suggests: testthat, MASS, glmnet, penalized, ordinalgmifs, nnet

Suggests: testthat, MASS, glmnet (>= 2.0-1), penalized, ordinalgmifs,

Description: Fits ordinal regression models with elastic net penalty. Supported
models include cumulative logit, probit, cauchit, and complementary log-log.

person("Bret", "Hanlon", email = "hanlon@stat.wisc.edu", role = "ths"))

person("Paul", "Rathouz", email = "rathouz@biostat.wisc.edu", role = "ths"),
Description: Fits ordinal regression models with elastic net penalty by coordinate descent.
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.