Depends: R (>= 2.15.0), BBmisc (>= 1.9), methods
Imports: brew, checkmate (>= 1.5.1), DBI, digest, fail (>= 1.2),
parallel, RSQLite (>= 1.0.0), sendmailR, stats, stringr, utils

Depends: R (>= 3.0.0), BBmisc (>= 1.9), methods
Imports: backports (>= 1.1.1), brew, checkmate (>= 1.8.0), data.table
sendmailR, stats, stringi (>= 0.4-1), utils

Title: Software for the Creation of High Density Linkage Maps in
Description: Algorithms that build on the 'OneMap' package to create linkage
maps from high density data in outcrossing species in reasonable time frames.
Authors@R: c(person("Bastian", "Schiffthaler", role = c("aut","cre"),
person("Gabriel", "Margarido", role = "aut", email =
"gramarga@usp.br"), person("Marcelo", "Mollinari", role =
c("aut"), email = "mmollina@usp.br"), person("Karl",
"Broman", role = "ctb", email = "kbroman@biostat.wisc.edu"),
person("Augusto", "Garcia", role = c("aut", "ctb"), email =
LinkingTo: Rcpp (>= 0.10.5), RcppArmadillo (>= 0.7.700)
Author: Bastian Schiffthaler [aut, cre],

Title: Bayesian Feature Allocation Model for Tumor Heterogeneity
Author: Juhee Lee, Peter Mueller, Subhajit Sengupta, Kamalakar Gulukota, Yuan Ji
Description: The package implements a Bayesian feature allocation model for inference on tumor heterogeneity using next-generation sequencing data. The model identifies the subclonal copy number and single nucleotide mutations at a selected set of loci and provides inference on genetic tumor variation.

Description: A Bayesian feature allocation model is implemented for inference on tumor heterogeneity using next-generation sequencing data. The model identifies the subclonal copy number and single nucleotide mutations at a selected set of loci and provides inference on genetic tumor variation.

Title: Bayesian analysis of haplotype association using Markov Chain
Author: Raquel Iniesta and Victor Moreno
Description: The package BayHap performs simultaneous estimation of
uncertain haplotype frequencies and association with haplotypes
based on generalized linear models for quantitative, binary and
survival traits. Bayesian statistics and Markov Chain Monte
Carlo techniques are the theoretical framework for the methods
of estimation included in this package. Prior values for model
parameters can be included by the user. Convergence diagnostics
and statistical and graphical analysis of the sampling output