reshape2, S4Vectors, shiny, ggseqlogo, ggtree, foreach,

ggplot2, ggraph, igraph, network, phangorn, stringdist,
visNetwork, reshape2, S4Vectors, shiny, ggseqlogo, ggtree,
dplyr, VennDiagram, futile.logger, future, future.apply, tools

Imports: methods, RColorBrewer, ape, ggnetwork, ggplot2, ggraph,
igraph, network, phangorn, stringdist, visNetwork, reshape2,
S4Vectors, shiny, ggseqlogo, ggtree, dplyr, VennDiagram,
futile.logger, future, future.apply, tools, ShortRead,

Suggests: BiocManager, testthat, knitr, rmarkdown

person("Jacob", "Cardenas", email = "jacob.cardenas@bswhealth.org",
role = c("aut", "cre")), person("Jacob", "Turner", role = "aut"),
person("Derek", "Blankenship", role = "aut")
Description: A set of functions to generate and format results from statistical
analyses of a wide range of high throughput experiments that can then
be uploaded into the 'BART' (Bio-Statistical Analysis Reporting Tool) 'shiny'
app <https://github.com/jcardenas14/BART>. The app provides users with tools
to visualize and efficiently sift through large amounts of data and results.
Imports: clValid (>= 0.6-6), data.table (>= 1.10.4), fastcluster (>=

Title: An implementation of the MOSS algorithm for the analysis of GWAS
Author: Matthew Friedlander and Laurent Briollais
Description: The MOSS algorithm is a Bayesian variable selection
procedure that is applicable to GWAS data. It identifies
combinations of the best predictive SNPs associated with the
response. It also performs a hierarchical log-linear model
search to identify the most relevant associations among the
Packaged: 2013-04-15 14:34:47 UTC; mfriedlander

Title: Functions for the Bayesian analysis of GWAS data
Author: Matthew Friedlander, Adrian Dobra, Helene Massam, and Laurent Briollais
Description: The genMOSS package implements the Mode Oriented Stochastic Search (MOSS) algorithm as well as a simple moving window approach to look for combinations of SNPs that are associated with a response.
Packaged: 2013-09-19 09:54:18 UTC; mfriedlander

Title: Functions for the Bayesian Analysis of GWAS Data
Description: Implements the Mode Oriented Stochastic Search (MOSS) algorithm as well as a simple moving window approach to look for combinations of SNPs that are associated with a response.