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>.

Random Forest with Canonical Correlation Analysis (RFCCA) is a 
  random forest method for estimating the canonical correlations between two 
  sets of variables depending on the subject-related covariates. The trees are 
  built with a splitting rule specifically designed to partition the data to 
  maximize the canonical correlation heterogeneity between child nodes. The 
  method is described in Alakus et al. (2021) <doi:10.1093/bioinformatics/btab158>. 
  'RFCCA' uses 'randomForestSRC' package (Ishwaran and Kogalur, 2020) by 
  freezing at the version 2.9.3. The custom splitting rule feature is utilised 
  to apply the proposed splitting rule. The 'randomForestSRC' package implements 
  'OpenMP' by default, contingent upon the support provided by the target 
  architecture and operating system. In this package, 'LAPACK' and 'BLAS' 
  libraries are used for matrix decompositions.

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> .

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>.