Extension of the snow package supporting fault tolerant and reproducible applications, as well as supporting easy-to-use parallel programming - only one function is needed. Dynamic cluster size is also available.

Run 'SQL' queries across 'Snowflake', 'Amazon Redshift', 'PostgreSQL', 'SQLite', and 'DuckDB' from R with a single function. Optionally stream and cache large query results to a local 'DuckDB' database for efficient work with larger-than-memory datasets.

Population genetics package for designing diagnostic panels. 
    Candidate markers, marker combinations, and different panel sizes are 
    assessed for how well they can predict the source population of known 
    samples. Requires a genotype file of candidate markers in STRUCTURE format. 
    Methods for population cross-validation are described in Jombart (2008) 
    <doi:10.1093/bioinformatics/btn129>.

To automated functional annotation of genetic variants and linked proxies. 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' authored by Knaus and Grünwald (2017) <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.