Use Monte-Carlo and K-fold cross-validation coupled with machine-
    learning classification algorithms to perform population assignment, with
    functionalities of evaluating discriminatory power of independent training
    samples, identifying informative loci, reducing data dimensionality for genomic
    data, integrating genetic and non-genetic data, and visualizing results.

Routines for re-scaling isotope maps using known-origin tissue isotope data, assigning origin of unknown samples, and summarizing and assessing assignment results. Methods are adapted from Wunder (2010, in ISBN:9789048133536) and Vander Zanden, H. B. et al. (2014) <doi:10.1111/2041-210X.12229> as described in Ma, C. et al. (2020) <doi:10.1111/2041-210X.13426>.

Fit various smoothing spline models. Includes an ssr() function for smoothing 
    spline regression, an nnr() function for nonparametric nonlinear regression, an snr() 
    function for semiparametric nonlinear regression, an slm() function for semiparametric 
    linear mixed-effects models, and an snm() function for semiparametric nonlinear 
    mixed-effects models. See Wang (2011) <doi:10.1201/b10954> for an overview.

Clinical trial design for subgroup selection in three-stage group
             sequential trial as described in Lai, Lavori and Liao (2014,
	     <doi:10.1016/j.cct.2014.09.001>). Includes facilities for design,
	     exploration and analysis of such trials. An implementation of
	     the initial DEFUSE-3 trial is also provided as a vignette.

An iterative implementation of a recursive binary partitioning algorithm to measure pairwise dependence with a modular design that allows user specification of the splitting logic and stop criteria. Helper functions provide suggested versions of both and support visualization and the computation of summary statistics on final binnings. For a thorough discussion and demonstration of the algorithm, see Salahub and Oldford (2025) <doi:10.1002/sam.70042>.

A user-friendly 'shiny' application to explore statistical associations and visual patterns in multivariate datasets. The app provides interactive correlation networks, bivariate plots, and summary tables for different types of variables (numeric and categorical). It also supports optional survey weights and range-based filters on association strengths, making it suitable for the exploration of survey and public data by non-technical users, journalists, educators, and researchers. For background and methodological details, see Soetewey et al. (2025) <doi:10.1016/j.softx.2025.102483>.