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.

Functions for creating and manipulating RNA secondary structure plots.

Calculate RNNI distance between and manipulate with ranked trees. RNNI stands for Ranked Nearest Neighbour Interchange and is an extension of the classical NNI space (space of trees created by the NNI moves) to ranked trees, where internal nodes are ordered according to their heights (usually assumed to be times). The RNNI distance takes the tree topology into account, as standard NNI does, but also penalizes changes in the order of internal nodes, i.e. changes in the order of times of evolutionary events. For more information about the RNNI space see: Gavryushkin et al. (2018) <doi:10.1007/s00285-017-1167-9>, Collienne & Gavryushkin (2021) <doi:10.1007/s00285-021-01567-5>, Collienne et al. (2021) <doi:10.1007/s00285-021-01685-0>, and Collienne (2021) <http://hdl.handle.net/10523/12606>.