The ordinal forest (OF) method allows ordinal regression with high-dimensional
  and low-dimensional data. After having constructed an OF prediction rule using a training dataset, 
  it can be used to predict the values of the ordinal target variable for new observations.
  Moreover, by means of the (permutation-based) variable importance measure of OF, it is also
  possible to rank the covariates with respect to their importance in the prediction of the 
  values of the ordinal target variable.
  OF is presented in Hornung (2020).
  NOTE: Starting with package version 2.4, it is also possible to obtain class probability 
  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.
  The main functions of the package are: ordfor() (construction of OF) and predict.ordfor() 
  (prediction of the target variable values of new observations).
  References:
  Hornung R. (2020) Ordinal Forests. Journal of Classification 37, 4–17. 
  <doi:10.1007/s00357-018-9302-x>.

Provides a function for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.

It implements functions for simulation and estimation of the ordinal latent block model (OLBM), as described in Corneli, Bouveyron and Latouche (2019).

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>.