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r-cran-qtl package

r-cran-qtl 1.40-8-1build1 source package in Ubuntu

Changelog

r-cran-qtl (1.40-8-1build1) artful; urgency=medium

  * No-change rebuild to pick up r-api-3.4

 -- Graham Inggs <email address hidden>  Wed, 27 Sep 2017 09:45:50 +0000

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Uploaded by:
Graham Inggs
Uploaded to:
Artful
Original maintainer:
Debian Med
Architectures:
any
Section:
misc
Urgency:
Medium Urgency

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Series Pocket Published Component Section

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File Size SHA-256 Checksum
r-cran-qtl_1.40-8.orig.tar.gz 4.2 MiB 57a3494a0d3bd7f095382070f4c179fd1228da585cd3e623f627029f450f7215
r-cran-qtl_1.40-8-1build1.debian.tar.xz 5.9 KiB f3084631f5f076305229ea1255e22fece25276679bdb28d1b9050e6d1d3b7162
r-cran-qtl_1.40-8-1build1.dsc 2.1 KiB 4bf98efe52cafec248508e9ce0f23be4d5647de11dd628b5097912e1392ad768

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Binary packages built by this source

r-cran-qtl: GNU R package for genetic marker linkage analysis

 R/qtl is an extensible, interactive environment for mapping quantitative
 trait loci (QTLs) in experimental crosses. It is implemented as an
 add-on-package for the freely available and widely used statistical
 language/software R (see http://www.r-project.org).
 .
 The development of this software as an add-on to R allows one to take
 advantage of the basic mathematical and statistical functions, and
 powerful graphics capabilities, that are provided with R. Further,
 the user will benefit by the seamless integration of the QTL mapping
 software into a general statistical analysis program. The goal is to
 make complex QTL mapping methods widely accessible and allow users to
 focus on modeling rather than computing.
 .
 A key component of computational methods for QTL mapping is the hidden
 Markov model (HMM) technology for dealing with missing genotype data. The
 main HMM algorithms, with allowance for the presence of genotyping errors,
 for backcrosses, intercrosses, and phase-known four-way crosses
 were implemented.
 .
 The current version of R/qtl includes facilities for estimating
 genetic maps, identifying genotyping errors, and performing single-QTL
 genome scans and two-QTL, two-dimensional genome scans, by interval
 mapping (with the EM algorithm), Haley-Knott regression, and multiple
 imputation. All of this may be done in the presence of covariates (such
 as sex, age or treatment). One may also fit higher-order QTL models by
 multiple imputation.

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