A simple tool for the bulk creation and testing of parametric survival models. 
    Simply provide 'fssg' with a formula and some data, and let it identify the best distributions for you.

Multithreaded serialization of compressed data frames using the 'fst' format. The
    'fst' format allows for full random access of stored data and a wide range of compression
    settings using the LZ4 and ZSTD compressors.

Provides efficient methods to compute local and genome wide
    genetic distances (corresponding to the so called Hudson Fst
    parameters) through moment method, perform chromosome segmentation
    into homogeneous Fst genomic regions, and selection sweep detection
    for multi-population comparison. When multiple profile segmentation is
    required, the procedure can be parallelized using the future package.

Has two functions to help with calculating feature selection stability. 'Lump' is a function that groups subset vectors into a dataframe, and adds NA to shorter vectors so they all have the same length.
    'ASM' is a function that takes a dataframe of subset vectors and the original vector of features as inputs, and calculates the Stability of the feature selection.
    The calculation for 'asm' uses the Adjusted Stability Measure proposed in: 'Lustgarten', 'Gopalakrishnan', & 'Visweswaran' (2009)<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2815476/>.

The 'fstlib' library provides multithreaded serialization of compressed data frames using the
    'fst' format. The 'fst' format allows for random access of stored data and compression with the 'LZ4' and 'ZSTD'
    compressors.

A program to generate smoothed quantiles for the Fst-heterozygosity distribution. Designed for use with large numbers of loci (e.g., genome-wide SNPs). The best case for analyzing the Fst-heterozygosity distribution is when many populations (>10) have been sampled. See Flanagan & Jones (2017) <doi:10.1093/jhered/esx048>.

Functions for sequencing studies allowing for multiple functional annotation scores. Score type tests and an efficient perturbation method are used for individual gene/large gene-set/genome wide analysis. Only summary statistics are needed.