r-cran-qtl 1.60-1 source package in Ubuntu

Changelog

r-cran-qtl (1.60-1) unstable; urgency=medium

  * New upstream version

 -- Andreas Tille <email address hidden>  Mon, 26 Jun 2023 16:45:05 +0200

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Debian R Packages Maintainers
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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_1.60-1.dsc 2.0 KiB 6c1a386eecb5748999ce1865fba88faf5a104a026357fe1074b6695d06de8a0a
r-cran-qtl_1.60.orig.tar.gz 4.2 MiB 8e9e5dfe2c6a76d4f69fb27add93ed0859ed3eaa23347310c2b9e3f07359d8ad
r-cran-qtl_1.60-1.debian.tar.xz 7.0 KiB d9a9e054e06fd6ecfe75c161dddc8fe8fd80c9efd63d6d408c04f69d168604cf

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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