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Source: r-cran-qtl
Maintainer: Debian Med Packaging Team <debian-med-packaging@lists.alioth.debian.org>
Uploaders: Steffen Moeller <moeller@debian.org>,
Andreas Tille <tille@debian.org>
Section: gnu-r
Priority: optional
Build-Depends: debhelper (>= 9),
dh-r,
r-base-dev
Standards-Version: 3.9.8
Vcs-Browser: https://anonscm.debian.org/viewvc/debian-med/trunk/packages/R/r-cran-qtl/trunk/
Vcs-Svn: svn://anonscm.debian.org/debian-med/trunk/packages/R/r-cran-qtl/trunk/
Homepage: https://cran.r-project.org/package=qtl
Package: r-cran-qtl
Architecture: any
Depends: ${shlibs:Depends},
${misc:Depends},
${R:Depends}
Recommends: ruby,
${R:Recommends}
Suggests: ${R:Suggests}
Description: 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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