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
MD5	1E93BD00993FE69A75FEEB85F5FAD90C
PackageArch	sparc64
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.fc15
PackageVersion	1.19.20
SHA-1	DD76C2A82CB487A6256FF0DBCE81CB4E99547C49
SHA-256	0D61B794E7997D10AFB00C7F5E3B6B9502BED903C96D526F81183ADFC59F6E1C

Key	Value
MD5	E0AA4F16E0423A2EE509FC1E7D521E97
PackageArch	s390x
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	2.fc15
PackageVersion	1.19.20
SHA-1	2D0EF7590ECC5A0EBD66C0825E6DB99CA26418FB
SHA-256	A50A809EE2AC10355976B245509A7F4C57C4174E107E159E0D42048159040CCD

Key	Value
MD5	D7BB513DBD80233894DA88B08FA16F7D
PackageArch	sparcv9
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.fc15
PackageVersion	1.19.20
SHA-1	E2D9474EF8D76BC7716FDF6342E03E488D7FD3D7
SHA-256	575DD04E31E03A8909058A969310231117315E512DD04E82E2310E8058A273B3

Key	Value
MD5	C69799890DB8DA3AC9C9FE99EC5EC9C6
PackageArch	ppc64
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	Koji
PackageName	R-qtl
PackageRelease	2.fc15
PackageVersion	1.19.20
SHA-1	27375487F83723C9449849B18BBF50C94FDEB1BD
SHA-256	3DCAC35AA1E67AD8D5BB42D824A0EAB44D2252345BA3A9BB938E1E1F0A747DD6

Key	Value
MD5	9F9FB11353DCF6AEA2A04A4005677469
PackageArch	s390
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	2.fc15
PackageVersion	1.19.20
SHA-1	DE6F241BEDD269226DCDE31C00F5E07D503EAD54
SHA-256	3EAE25346286AA1E9B948E9CC38D4B2904011FD5E1043C686D4F269C751CDF87

Key	Value
MD5	FF070A1607B9973EAB3F210E1DE8AA93
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	Koji
PackageName	R-qtl
PackageRelease	2.fc15
PackageVersion	1.19.20
SHA-1	E6CD7AD5E0FB810F064A6288D9F156E893F81B4B
SHA-256	3D60122A2B4BC9675A8822EA4707BF24F29A657EDB1DA65074DB62F55E0DB6E7