Fast OpenMP parallel computing of Breiman's random forests for univariate, multivariate, unsupervised, survival, competing risks, class imbalanced classification and quantile regression. New Mahalanobis splitting for correlated outcomes.  Extreme random forests and randomized splitting.  Suite of imputation methods for missing data.  Fast random forests using subsampling. Confidence regions and standard errors for variable importance. New improved holdout importance. Case-specific importance.  Minimal depth variable importance. Visualize trees on your Safari or Google Chrome browser. Anonymous random forests for data privacy.

Functions for assessing variable relations and associations 
    prior to modeling with a Random Forest algorithm (although these are 
    relevant for any predictive model).
    Metrics such as partial correlations and variance inflation factors
    are tabulated as well as plotted for the user. A function is available
    for tuning the main Random Forest hyper-parameter based on model performance 
    and variable importance metrics. This grid-search technique provides
    tables and plots showing the effect of the main hyper-parameter on each 
    of the assessment metrics. It also returns each of the evaluated models 
    to the user. The package also provides superior variable importance plots 
    for individual models. All of the plots are developed so that the 
    user has the ability to edit and improve further upon the 
    plots. Derivations and methodology are described in Bladen (2022) 
    <https://digitalcommons.usu.edu/etd/8587/>.

Provides an accessible and efficient implementation of a randomized
    feature and bootstrap-enhanced Gaussian naive Bayes classifier. The method
    combines stratified bootstrap resampling with random feature subsampling and
    aggregates predictions via posterior averaging. Support is provided for
    mixed-type predictors and parallel computation. Methods are described in
    Srisuradetchai (2025) <doi:10.3389/fdata.2025.1706417> "Posterior averaging
    with Gaussian naive Bayes and the R package RandomGaussianNB for big-data
    classification".