Package: polysat
Version: 1.7-3
Date: 2018-05-28
Title: Tools for Polyploid Microsatellite Analysis
Authors@R: c(person("Lindsay V.", "Clark", email = "lvclark@illinois.edu", role = c("aut", "cre")),
person("Handunnethi Nihal", "de Silva", role = "ctb"))
Author: Lindsay V. Clark [aut, cre],
Handunnethi Nihal de Silva [ctb]
Maintainer: Lindsay V. Clark <lvclark@illinois.edu>
Imports: methods, stats, utils, grDevices, graphics, Rcpp
Suggests: ade4, adegenet, ape
LinkingTo: Rcpp
Description: A collection of tools to handle microsatellite data of
any ploidy (and samples of mixed ploidy) where allele copy number is not
known in partially heterozygous genotypes.  It can import and export data in
ABI 'GeneMapper', 'Structure', 'ATetra', 'Tetrasat'/'Tetra', 'GenoDive', 'SPAGeDi',
'POPDIST', 'STRand', and binary presence/absence formats.  It can calculate
pairwise distances between individuals using a stepwise mutation model or
infinite alleles model, with or without taking ploidies and allele frequencies
into account.  These distances can be used for the calculation of clonal
diversity statistics or used for further analysis in R.  Allelic diversity
statistics and Polymorphic Information Content are also available.  polysat can
assist the user in estimating the ploidy of samples, and it can estimate allele
frequencies in populations, calculate pairwise or global differentiation statistics
based on those frequencies, and export allele frequencies to 'SPAGeDi' and 'adegenet'.
Functions are also included for assigning alleles to isoloci in cases where one pair
of microsatellite primers amplifies alleles from two or more independently
segregating isoloci.  polysat is described by Clark and Jasieniuk (2011)
<doi:10.1111/j.1755-0998.2011.02985.x> and Clark and Schreier (2017)
<doi:10.1111/1755-0998.12639>.
License: GPL-2
URL: https://github.com/lvclark/polysat/wiki
NeedsCompilation: yes
Packaged: 2018-06-03 23:14:23 UTC; Lindsay
Repository: CRAN
Date/Publication: 2018-06-07 19:49:58 UTC

Title: Polytomous logistic regression for fixed and mixed effects
Author: Antti Arppe, with ideas and input from R. Harald Baayen
Description: Logistic regression modeling for polytomous settings (more
than two categorical outcomes) with both fixed and mixed effect
predictors, and univariate analysis of categorical, unordered
Depends: R(>= 2.10.1), stats, MASS, Hmisc, lme4