Feature Selection with Regularized Random Forest. This
    package is based on the 'randomForest' package by Andy Liaw.
    The key difference is the RRF() function that builds a
    regularized random forest. Fortran original by Leo Breiman 
    and Adele Cutler, R port by Andy Liaw and Matthew Wiener, 
    Regularized random forest for classification by Houtao Deng, 
    Regularized random forest for regression by Xin Guan.
    Reference: Houtao Deng (2013) <doi:10.48550/arXiv.1306.0237>.

Performs species distribution modeling for rare species with unprecedented accuracy (Mondanaro et al., 2023 <doi:10.1111/2041-210X.14066>) and finds the area of origin of species and past contact between them taking climatic variability in full consideration (Mondanaro et al., 2025 <doi:10.1111/2041-210X.14478>).

Testing and inference for regression models using residual randomization methods. The basis of inference is an invariance assumption on the regression errors, e.g., clustered errors, or doubly-clustered errors.

Collection of functions for fitting distributions to given data or
    by known quantiles. Two main functions fit.perc() and fit.cont() provide
    users a GUI that allows to choose a most appropriate distribution without
    any knowledge of the R syntax. Note, this package is a part of the 'rrisk'
    project.

We implement full-ranked, rank-penalized, and adaptive nuclear norm penalized estimation methods using multivariate mixture models proposed by Kang, Chen, and Yao (2022+).

Fit the reduced-rank multinomial logistic regression model for Markov
    chains developed by Wang, Abner, Fardo, Schmitt, Jicha, Eldik and Kryscio
    (2021)<doi:10.1002/sim.8923> in R. It combines the ideas of multinomial
    logistic regression in Markov chains and reduced-rank. It is very useful in 
    a study where multi-states model is assumed and each transition among the 
    states is controlled by a series of covariates. The key advantage is to 
    reduce the number of parameters to be estimated. The final coefficients for 
    all the covariates and the p-values for the interested covariates will be 
    reported. The p-values for the whole coefficient matrix can be calculated by 
    two bootstrap methods.