Result for 06ABE303BE9258BAF3F65196C06DC75BA932FA17

Query result

Key Value
FileName./usr/lib64/R/library/qtl/help/est.rf.html
FileSize91
MD55558584DEB62C95B0A81E5945A7BF68D
SHA-106ABE303BE9258BAF3F65196C06DC75BA932FA17
SHA-2564847B74A90B9ECF16B60EFB676FD8A5AAEE5CB086F0930E80F16D604DDC2DE09
SSDEEP3:qVZxVsNjJpDQJu+OWBJWEBbZWu:qzxVkHWBJDB9t
TLSHT164B012E698B0003729033D10FEC4321369414910B80E4D10D1840CEC44E031DC801038
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