r-cran-qtl 1.66-1 source package in Ubuntu

Changelog

r-cran-qtl (1.66-1) unstable; urgency=medium

  * New upstream version

 -- Andreas Tille <email address hidden>  Tue, 09 Jan 2024 08:36:58 +0100

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Uploaded by:
Debian R Packages Maintainers
Uploaded to:
Sid
Original maintainer:
Debian R Packages Maintainers
Architectures:
any
Section:
misc
Urgency:
Medium Urgency

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File Size SHA-256 Checksum
r-cran-qtl_1.66-1.dsc 2.0 KiB 889a14fb5bb4825cac1653f92fa80fcc5df0859bd6e6c10055cae742221ec877
r-cran-qtl_1.66.orig.tar.gz 4.2 MiB d46a7d49f2d0875c0c1cba77c993f995e7cac4db5796dfb1c62d9fa4eb60d681
r-cran-qtl_1.66-1.debian.tar.xz 7.1 KiB a5ab18e6c1edad755d95f50f24cc9b4a0dcfbc143f80fc2fede860c17f8ae14b

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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.

r-cran-qtl-dbgsym: debug symbols for r-cran-qtl