Result for 067F548A9233788FA6A3CB1A6F1B6771A96CFE90

Query result

Key Value
FileName./usr/lib/R/site-library/qtl/help/qtl.rdx
FileSize4554
MD5E3FAD1ADEB3D9785EEA6D1A3AED3F0D8
SHA-1067F548A9233788FA6A3CB1A6F1B6771A96CFE90
SHA-2564B31BF6669A096E988E095FD1CCA333421F83FB3B669FE342FD5849B0B9FA5EC
SSDEEP96:clc3pt55O/umN++0ngOfaslxh8fdgsYfNfH4JaX3r9P/tD:clYpNKN+Nn5faARfFoaX39tD
TLSHT1AA917D838BE31B53BE7247081DC3716249C34A52A06507D67731AA24FEFAA92377561D
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize5517332
MD56E26AB1A9D38F7A12D6FBB317AC11EB3
PackageDescriptionGNU 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.
PackageMaintainerDebian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageNamer-cran-qtl
PackageSectiongnu-r
PackageVersion1.44-9-1
SHA-155296977B5FFED8AEF8258E1F82EE47F6717FBC4
SHA-25664EE45B5C8E3FC885175BE4D6F0775B12F40A8967482F6DE56222EE6A18E3459