Result for 012EDE46231441C7E368E4CEEED1146F2675A179

Query result

Key Value
FileName./usr/lib/R/site-library/qtl/Meta/package.rds
FileSize1011
MD51AC7148AC19F6EBB87F23757FC50F7B8
SHA-1012EDE46231441C7E368E4CEEED1146F2675A179
SHA-25689D5A7F0AF84CBF0B9C2B2C8DF037A44C80FE51E6635CA202E8B0EB7C670E4B4
SSDEEP24:XHQ1ilskb8+E+hCV8D7SF865whJ4J/NovxDVp4bo7ZJ96:XHQ1A5E+82S9whuJ/NslVp/ZJ96
TLSHT1F711A815C0C0451FCCB2FCB6A610C3CE9DD99A12E717C99479465814DF25FE1919B355
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
FileSize5022486
MD539EE8384121F9B92017413545BB75057
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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamer-cran-qtl
PackageSectiongnu-r
PackageVersion1.37-11-1
SHA-17406E4D5A17C1F06FE9D44EA3752F48A64AA0D1C
SHA-256D8D780F0FC201FAB7B7EBD04ACB064A504DC0134F3190AF7310CBBC6598081EE