Performs Stratified Covariate Balancing with Markov blanket feature selection and use of synthetic cases. See Alemi et al. (2016) <DOI:10.1111/1475-6773.12628>.

A toolkit for stratified medicine, subgroup identification, and precision medicine.
    Current tools include (1) filtering models (reduce covariate space), (2) patient-level estimate
    models (counterfactual patient-level quantities, such as the conditional average treatment effect), 
    (3) subgroup identification models (find subsets of patients with similar treatment effects), 
    and (4) treatment effect estimation and inference (for the overall population and discovered 
    subgroups). These tools can be customized and are directly used in PRISM 
    (patient response identifiers for stratified medicine; Jemielita and Mehrotra 2019
    <arXiv:1912.03337>. This package is in beta and will be continually updated.

Random Forest-like tree ensemble that works with groups of predictor variables. When building a tree, a number of variables is taken randomly from each group separately, thus ensuring that it considers variables from each group for the splits. Useful when rows contain information about different things (e.g. user information and product information) and it's not sensible to make a prediction with information from only one group of variables, or when there are far more variables from one group than the other and it's desired to have groups appear evenly on trees.
    Trees are grown using the C5.0 algorithm rather than the usual CART algorithm. Supports parallelization (multithreaded), missing values in predictors, and categorical variables (without doing One-Hot encoding in the processing). Can also be used to create a regular (non-stratified) Random Forest-like model, but made up of C5.0 trees and with some additional control options.
    As it's built with C5.0 trees, it works only for classification (not for regression).

Provides functions for stratified sampling and assigning custom labels to data, ensuring randomness within groups. The package supports various sampling methods such as stratified, cluster, and systematic sampling. It allows users to apply transformations and customize the sampling process. This package can be useful for statistical analysis and data preparation tasks.