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.

When doing association analysis one does not always have the 
   genotypes for the control population.  In such cases it may be 
   necessary to fall back on frequency based tests using well known sources 
   for the frequencies in the control population, for instance, from the 
   1000 Genomes Project.  The Allele Frequency Comparison ('AssocAFC') package 
   performs multiple rare variant association analyses in both population and 
   family-based GWAS (Genome-Wide Association Study) designs. It includes 
   three score tests that are based on the difference of the sum of allele 
   frequencies between cases and controls.  Two of these tests, Wcorrected() 
   and Wqls(), are collapsing-based tests and suffer from having protective 
   and risk variants. The third test, afcSKAT(), is a score test that 
   overcomes the mix of SNP (Single-Nucleotide Polymorphism) effect 
   directions. For more details see 
   Saad M and Wijsman EM (2017) <doi:10.1093/bib/bbx107>.