Parents (Total: 3)

The searched file hash is included in 3 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	0F05AEE449E4D4DE12D45F24C965FFBA
PackageArch	i386
PackageDescription	R-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.
PackageMaintainer	Fedora Project
PackageName	R-qtl
PackageRelease	1.el5
PackageVersion	1.40.8
SHA-1	C43BAB98CD935E793324EA511D5B7D57851815F6
SHA-256	AFF5E6CBC8775FE95E35CD8BA0D0D5DB0CCA957D22CA10459D42B2FBE236EF37

Key	Value
MD5	580248D94BA3E9D3B9AA7871F5BBECB3
PackageArch	x86_64
PackageDescription	R-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.
PackageMaintainer	Fedora Project
PackageName	R-qtl
PackageRelease	1.el5
PackageVersion	1.40.8
SHA-1	3B5222BCA7481DD723FDDC8DDEE0B8A1189729B6
SHA-256	9500EB0D6E664F96CFB0C6711108753F664974AAD38B737C0F29F29041CCEA13

Key	Value
MD5	B15D66FDD7C0D5551D281B706C3B38D3
PackageArch	ppc
PackageDescription	R-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.
PackageMaintainer	Fedora Project
PackageName	R-qtl
PackageRelease	1.el5
PackageVersion	1.40.8
SHA-1	FBDCB895C1572F670C5826876BAA32CF8AD4BAE6
SHA-256	67E81D17201EACFD0992486A47BC7836A9FDDF366DD206952EDDA3BEAD0F5F7B