Result for 03A0A804B83379172063268AA301440D335E33BE

Query result

Key Value
FileName./usr/lib64/R/library/qtl/Meta/data.rds
FileSize392
MD580FF1F95488506072DC3B58A797B12B4
SHA-103A0A804B83379172063268AA301440D335E33BE
SHA-256488DF1CAD6726573515F668C588EF592FBF34962E8A94F3A3E3AF9C2379F9855
SSDEEP12:XU8m8UBf6aWr62QYKsc/qxH/llGil0zUge8/Sl:Xq8UBfWrFuscU/l4Q4qiW
TLSHT141E0F18147F08474D1C0E9F1B337C946D7C957205668C1113DA48B618F268041A71E5A
hashlookup:parent-total6
hashlookup:trust80

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Parents (Total: 6)

The searched file hash is included in 6 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5744A1389F844A7C502B8FC09AAFBD2E3
PackageArchppc
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.
PackageMaintainerKoji
PackageNameR-qtl
PackageRelease1.fc16
PackageVersion1.21.2
SHA-1A705BFC5FF360DA0FF8C3F5758366580271CA6A3
SHA-25676C59DAFD8D73F3C52C978C0AB418E4B7C47D94068DF0A049087D3F3F22FFF52
Key Value
MD562F0AE0C3FCD8AD7D69FA5460F54CEA6
PackageArcharmv5tel
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
PackageRelease2.fc15
PackageVersion1.19.20
SHA-14B71E3300E49EF76E725AF2CCDE002F7289576B6
SHA-256BA85487D964C990FC20921CE6F228F70D049585A1B3FC0AE3C6932D5C211712D
Key Value
MD569DEBDDEB9D5135836A51F93C5D53669
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
PackageRelease1.fc16
PackageVersion1.21.2
SHA-19611FFE4C83F0B6E73FA20AA7AE0AC363A5C6308
SHA-2569F876C74B15D955F1F52A1AAE08092775B7B9136FB7503DC6AD22661A667537A
Key Value
MD54C25AAC79ABE8CAC3F9E8D5D1F29C5F4
PackageArchppc64
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.
PackageMaintainerKoji
PackageNameR-qtl
PackageRelease1.fc16
PackageVersion1.21.2
SHA-17C5847A653A2D52953FDE9103DF88790D1CA824B
SHA-2564512D7649BEAB1DB50811F12CF11023BAA264AC842F46608692171F0BD68FAC6
Key Value
MD59242542723C37DF0E5B0988B6D344A90
PackageArchs390
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
PackageRelease1.fc16
PackageVersion1.21.2
SHA-1F9DBEE3E3AA2E0D56809DA06F765B1EC0585F5A6
SHA-2561D608F808447F7C089FC4ED9CC622569380634307E87E0E5C5237A4EDA6E3BE7
Key Value
MD535B2D6B8E08665184C8A6ED68F0549CE
PackageArcharmv7hl
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
PackageRelease2.fc15
PackageVersion1.19.20
SHA-1F4892AB36BA239D40E127BA8BB7590D3FF2116A1
SHA-2569A8078ED302C4B35BBEDBFA0EDFCD0D4BF367FED3CD618051208245BDE08BDD2