A collection of fast statistical and utility functions for data analysis. Functions for regression, maximum likelihood, column-wise statistics and many more have been included. C++ has been utilized to speed up the functions. References: Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>.

Tools to perform random forest consensus clustering of different data types. The package is designed to accept a list of matrices from different assays, typically from high-throughput molecular profiling so that class discovery may be jointly performed. For references, please see Tao Shi & Steve Horvath (2006) <doi:10.1198/106186006X94072> & Monti et al (2003) <doi:10.1023/A:1023949509487> .

Methods for Resampling-based False Discovery Proportion control.
        A function is provided that provides simultaneous, multi-resolution
        False Discovery Exceedance (FDX) control as described in 
	Hemerik (2025) <doi:10.48550/arXiv.2509.02376>.

An R package for multiple imputation using chained random forests.
    Implemented methods can handle missing data in mixed types of variables by
    using prediction-based or node-based conditional distributions constructed
    using random forests. For prediction-based imputation, the method based on
    the empirical distribution of out-of-bag prediction errors of random forests
    and the method based on normality assumption for prediction errors of random
    forests are provided for imputing continuous variables. And the method based
    on predicted probabilities is provided for imputing categorical variables.
    For node-based imputation, the method based on the conditional distribution
    formed by the predicting nodes of random forests, and the method based on
    proximity measures of random forests are provided. More details of the
    statistical methods can be found in Hong et al. (2020) <arXiv:2004.14823>.

Provides the random ferns classifier by Ozuysal, Calonder, Lepetit and Fua (2009) <doi:10.1109/TPAMI.2009.23>, modified for generic and multi-label classification and featuring OOB error approximation and importance measure as introduced in Kursa (2014) <doi:10.18637/jss.v061.i10>.