To investigate the functional characteristics of selected SNPs and their vicinity genomic region. Linked SNPs in moderate to high linkage disequilibrium (e.g. r2>0.50) with the corresponding index SNPs will be selected for further analysis.

Functions to perform most of the common analysis in genome 
    association studies are implemented. These analyses include descriptive 
    statistics and exploratory analysis of missing values, calculation of 
    Hardy-Weinberg equilibrium, analysis of association based on generalized
    linear models (either for quantitative or binary traits), and analysis
    of multiple SNPs (haplotype and epistasis analysis). Permutation test 
    and related tests (sum statistic and truncated product) are also 
    implemented. Max-statistic and genetic risk-allele score exact 
    distributions are also possible to be estimated. The methods are 
    described in Gonzalez JR et al., 2007 <doi: 10.1093/bioinformatics/btm025>.

Is designed to interactively and reproducibly visualize and filter SNP
	(single-nucleotide polymorphism) datasets. This R-based implementation of SNP
	and genotype filters facilitates 
	an interactive and iterative SNP filtering pipeline, which can be documented
	reproducibly via Rmarkdown. 'SNPfiltR' contains functions for visualizing 
	various quality and missing data metrics for a SNP dataset, and then filtering
	the dataset based on user specified cutoffs.
	All functions take 'vcfR' objects as input, which can easily be
	generated by reading standard vcf (variant call format) files into R using 
	the R package 'vcfR' (Knaus and Grünwald) (<doi:10.1111/1755-0998.12549>). 
	Each 'SNPfiltR' function can return a newly filtered vcfR object, which can then be
	written to a local directory in standard vcf format using the 'vcfR' package,
	for downstream population genetic and phylogenetic analyses.

Generate knockoffs for genetic data and hidden Markov models.
  For more information, see the website below and the accompanying papers: 
  "Gene hunting with hidden Markov model knockoffs", Sesia et al., Biometrika, 2019, (<doi:10.1093/biomet/asy033>).
  "Multi-resolution localization of causal variants across the genome", Sesia et al., bioRxiv, 2019, (<doi:10.1101/631390>).