The 'rfacts' package is an R interface to the
  Fixed and Adaptive Clinical Trial Simulator ('FACTS')
  on Unix-like systems. It programmatically invokes 'FACTS' to run clinical
  trial simulations, and it aggregates simulation output data
  into tidy data frames. These capabilities provide end-to-end
  automation for large-scale simulation pipelines, and
  they enhance computational reproducibility.
  For more information on 'FACTS' itself,
  please visit <https://www.berryconsultants.com/software/>.

Download crash data from the National Highway Traffic Safety Administration and prepare it for research.

A collection of fast (utility) functions for data analysis. Column and row wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>. b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771--780. <doi:10.6339/JDS.201810_16(4).00006>. c) Chatzipantsiou C., Dimitriadis M., Papadakis M. and Tsagris M. (2020). Extremely Efficient Permutation and Bootstrap Hypothesis Tests Using Hypothesis Tests Using R. Journal of Modern Applied Statistical Methods, 18(2), eP2898. <doi:10.48550/arXiv.1806.10947>.

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.