Integrate sequencing data (Variant call format, e.g. VCF or BCF) or meta-analysis results in R. This package can help you (1) read VCF/BCF/BGEN files by chromosomal ranges (e.g. 1:100-200); (2) read RareMETAL summary statistics files; (3) read tables from a tabix-indexed files; (4) annotate VCF/BCF files; (5) create customized workflow based on Makefile.

Methods to generate random gene-gene association networks and simulate RNA-seq data from them, as described in Grimes and Datta (2021) <doi:10.18637/jss.v098.i12>. Includes functions to generate random networks of any size and perturb them to obtain differential networks. Network objects are built from individual, overlapping modules that represent pathways. The resulting network has various topological properties that are characteristic of gene regulatory networks. RNA-seq data can be generated such that the association among gene expression profiles reflect the underlying network. A reference RNA-seq dataset can be provided to model realistic marginal distributions. Plotting functions are available to visualize a network, compare two networks, and compare the expression of two genes across multiple networks.

Sequential triangular test for the arithmetic mean in one- and two-
    samples, proportions in one- and two-samples, and the Pearson's correlation
    coefficient.

A collection of Radix Tree and Trie algorithms for finding similar sequences and calculating sequence distances (Levenshtein and other distance metrics). This work was inspired by a trie implementation in Python: "Fast and Easy Levenshtein distance using a Trie." Hanov (2011) <http://stevehanov.ca/blog/index.php?id=114>.

Contains fast functions to calculate the exact Bayes posterior
    for the Sparse Normal Sequence Model, implementing the algorithms
    described in Van Erven and Szabo (2021,
    <doi:10.1214/20-BA1227>). For general hierarchical
    priors, sample sizes up to 10,000 are feasible within half an hour
    on a standard laptop. For beta-binomial spike-and-slab priors, a
    faster algorithm is provided, which can handle sample sizes of
    100,000 in half an hour. In the implementation, special care has
    been taken to assure numerical stability of the methods even for
    such large sample sizes.