Simple methods to generate attractive random colors. The random
  colors are from a wrapper of 'randomColor.js'
  <https://github.com/davidmerfield/randomColor>. In addition, it also generates
  optimally distinct colors based on k-means (inspired by 'IWantHue'
  <https://github.com/medialab/iwanthue>).

Classification and regression based on a forest of trees using random inputs, based on Breiman (2001) <DOI:10.1023/A:1010933404324>.

A set of tools to help explain which variables are most important in a random forests. Various variable importance measures are calculated and visualized in different settings in order to get an idea on how their importance changes depending on our criteria (Hemant Ishwaran and Udaya B. Kogalur and Eiran Z. Gorodeski and Andy J. Minn and Michael S. Lauer (2010) <doi:10.1198/jasa.2009.tm08622>, Leo Breiman (2001) <doi:10.1023/A:1010933404324>).

Random Hazard Forests (RHF) extend Random Survival
  Forests (RSF) by directly estimating the hazard function and by
  accommodating time-dependent covariates through counting-process
  style inputs. The package fits tree ensembles for dynamic survival
  prediction, returning hazard, cumulative hazard, integrated hazard,
  and related performance summaries for training and test data. The
  methods build on Random Survival Forests described by Ishwaran et
  al. (2008) <doi:10.1214/08-AOAS169> and on nonparametric hazard
  modeling with time-dependent covariates described by Lee et
  al. (2021) <doi:10.1214/20-AOS2028>.

Fits non-linear regression models on dependant data with Generalised Least Square (GLS) based Random Forest (RF-GLS) detailed in Saha, Basu and Datta (2021) <doi:10.1080/01621459.2021.1950003>.

Implements random forest Super Greedy Trees (SGTs) for
  regression. SGTs extend classification and regression tree splitting
  by fitting lasso-penalized local parametric models at tree nodes,
  producing sparse univariate and multivariate geometric cuts such as
  axis-aligned splits, hyperplanes, ellipsoids, hyperboloids, and
  interaction-based cuts.  Trees are grown best-split-first by
  selecting cuts that reduce empirical risk, and ensembles provide
  out-of-bag error estimation, prediction on new data, variable
  filtering, tuning of the hcut complexity parameter,
  coordinate-descent lasso fitting, variable importance, and local
  coefficient summaries. For the underlying method, 
  see Ishwaran (2026) <doi:10.1007/s10462-026-11541-6>.