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".

A bagging predictor based on generalized linear models (GLMs) is implemented. The method is published in 
   Song, Langfelder and Horvath (2013) <doi:10.1186/1471-2105-14-5>.

The Gene Ontology (GO) Consortium <https://geneontology.org/> organizes genes
  into hierarchical categories based on biological process (BP), molecular function (MF) and
  cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R.,
  Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) can leverage GO to perform
  ontological analysis of microarray and proteomics studies, typically generating a list of
  significant functional categories. The significance is traditionally determined by randomizing
  the input gene list to computing the false discovery rate (FDR) of the enrichment p-value for
  each category. We explore here the novel alternative of randomizing the GO database rather than
  the gene list.

Allows the user to conduct randomization-based inference for a wide variety of experimental scenarios. The package leverages a potential outcomes framework to output randomization-based p-values and null intervals for test statistics geared toward any estimands of interest, according to the specified null and alternative hypotheses. Users can define custom randomization schemes so that the randomization distributions are accurate for their experimental settings. The package also creates visualizations of randomization distributions and can test multiple test statistics simultaneously.