Author: Mathieu Gautier, Valentin Hivert and Renaud Vitalis
Title: Computing F-Statistics from Pool-Seq Data
Description: Functions for the computation of F-statistics from Pool-Seq data in population genomics studies. The package also includes several utilities to manipulate Pool-Seq data stored in standard format ('vcf' and 'rsync' files as obtained from the popular software 'VarScan' and 'PoPoolation' respectively) and perform conversion to alternative format (as used in the 'BayPass' and 'SelEstim' software).

Description: Functions for the computation of F-statistics from Pool-Seq data in population genomics studies. The package also includes several utilities to manipulate Pool-Seq data stored in standard format ('vcf' or 'rsync' files generated by the the 'PoPoolation' software) and perform conversion to alternative format (as used in the 'BayPass' and 'SelEstim' software).
Depends: R (>= 3.0), methods, utils, foreach, doParallel, parallel

Title: Computing f-Statistics and Building Admixture Graphs Based on
Allele Count or Pool-Seq Read Count Data
Description: Functions for the computation of f- and D-statistics (estimation of 'Fst', Patterson's 'F2', 'F3', 'F3*', 'F4' and D parameters) in population genomics studies from allele count or Pool-Seq read count data and for the fitting, building and visualization of admixture graphs. The package also includes several utilities to manipulate Pool-Seq data stored in standard format (e.g., such as 'vcf' files or 'rsync' files generated by the the 'PoPoolation' software) and perform conversion to alternative format (as used in the 'BayPass' and 'SelEstim' software). As of version 2.0, the package also includes utilities to manipulate standard allele count data (e.g., stored in 'TreeMix', 'BayPass' or 'SelEstim' format).
Imports: Rcpp (>= 1.0.5), methods, utils, foreach, doParallel,
parallel, DiagrammeR, ape, stats, zoo, Ryacas,

Description: Functions for calculating power and fitting regression models in studies where a biomarker is measured in "pooled" samples rather than for each individual. Approaches for handling measurement error follow the framework of Schisterman et al. (2010) <doi:10.1002/sim.3823>.
Imports: cubature, dplyr, dvmisc, ggplot2, ggrepel, mvtnorm, pracma,