Supports the modeling of ordinal random variables, 
    like the outcomes of races, via Softmax regression,
    under the Harville <doi:10.1080/01621459.1973.10482425> and
    Henery <doi:10.1111/j.2517-6161.1981.tb01153.x> models.

Ing and Lai (2011) <doi:10.5705/ss.2010.081> proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. The first two steps, OGA and HDIC, are used to sequentially select input variables and determine stopping rules, respectively. The third step, Trim, is used to delete irrelevant variables remaining in the second step. This package aims at fitting a high-dimensional linear regression model via OGA+HDIC+Trim.

Inference using a class of Hidden Markov models
    (HMMs) called 'oHMMed'(ordered HMM with emission densities 
    <doi:10.1186/s12859-024-05751-4>): The 'oHMMed' algorithms identify 
    the number of comparably homogeneous regions within observed sequences
    with autocorrelation patterns. These are modelled as discrete hidden
    states; the observed data points are then realisations of continuous
    probability distributions with state-specific means that enable
    ordering of these distributions. The observed sequence is labelled
    according to the hidden states, permitting only neighbouring states
    that are also neighbours within the ordering of their associated
    distributions. The parameters that characterise these state-specific
    distributions are then inferred. Relevant for application to genomic
    sequences, time series, or any other sequence data with serial
    autocorrelation.

Perform a Bayesian estimation of the ordinal exploratory 
    Higher-order General Diagnostic Model (OHOEGDM) for Polytomous Data 
    described by Culpepper, S. A. and Balamuta, J. J. (2021) <doi:10.1080/00273171.2021.1985949>.

Ordered homogeneity pursuit lasso (OHPL)
    algorithm for group variable selection proposed in Lin et al. (2017)
    <DOI:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits the
    homogeneity structure in high-dimensional data and enjoys the
    grouping effect to select groups of important variables
    automatically. This feature makes it particularly useful for
    high-dimensional datasets with strongly correlated variables,
    such as spectroscopic data.