Result for 07A26F22F18E2A8BD5609FE9F0C8E2A21FF3C8DA

Query result

Key Value
FileName./usr/lib64/R/library/qtl/html/addloctocross.html
FileSize3623
MD58B1EC8CC7627433CA385BED5D3BCDD09
SHA-107A26F22F18E2A8BD5609FE9F0C8E2A21FF3C8DA
SHA-256A56D08B07E2F7DA083118CC7400D28FA6660598C57683A5C5E792916749CABE5
SSDEEP96:1/ebQVGt36PDRMUsWj9244GTg51wsUQrS5UnE:kbQVsUsWjElnw2rS5UnE
TLSHT15271DAD894C5476502A1965FDF633958FBEF8322856E3C467C8FD222CA413BA476138F
hashlookup:parent-total2
hashlookup:trust60

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Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5AE66BDE8807716868ECC3029BD0956A7
PackageArchx86_64
PackageDescriptionR-qtl is an extensible, interactive environment for mapping quantitative trait loci (QTLs) in experimental crosses. Our 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. We have implemented the main HMM algorithms, with allowance for the presence of genotyping errors, for backcrosses, intercrosses, and phase-known four-way crosses. 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 and Haley-Knott regression.
PackageMaintainerFedora Project
PackageNameR-qtl
PackageRelease3.el8
PackageVersion1.48.1
SHA-1E5099A5B4596D3E0BE24BA4CF0F761A746B475B9
SHA-256A9F9A984C6DEA1D7990D68093E530425F46EEB0732F54CF1DAAC7E47F8DDE24C
Key Value
MD59193E7242FEAA900A4B427A11B23D7E3
PackageArchs390x
PackageDescriptionR-qtl is an extensible, interactive environment for mapping quantitative trait loci (QTLs) in experimental crosses. Our 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. We have implemented the main HMM algorithms, with allowance for the presence of genotyping errors, for backcrosses, intercrosses, and phase-known four-way crosses. 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 and Haley-Knott regression.
PackageMaintainerFedora Project
PackageNameR-qtl
PackageRelease3.el8
PackageVersion1.48.1
SHA-1A7D6889ADAEDF8284B0A0762BF261B6F6EF52EBD
SHA-256EA036F89098591048E558A34D80DFE54A92F0D007B7FC33CD4C66B566E0A8BEC