Parents (Total: 39)

The searched file hash is included in 39 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
FileSize	4076788
MD5	5DC9592A5A297E575744E0E854D23702
PackageDescription	GNU R package for genetic marker linkage analysis R/qtl is an extensible, interactive environment for mapping quantitative trait loci (QTLs) in experimental crosses. It is implemented as an add-on-package for the freely available and widely used statistical language/software R (see http://www.r-project.org). . The development of this software as an add-on to R allows one to take advantage of the basic mathematical and statistical functions, and powerful graphics capabilities, that are provided with R. Further, the user will benefit by the seamless integration of the QTL mapping software into a general statistical analysis program. The 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. The main HMM algorithms were implemented, 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.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	r-cran-qtl
PackageSection	gnu-r
PackageVersion	1.27-10-1
SHA-1	04B33AD1689247407376A95D5A325F07047C266C
SHA-256	93CF1870DE7169A8D627DCE20994EF6DD138F0C8F86D1ED764A1D3570B3D9921

Key	Value
MD5	8B59A3225F63F316459C8D7A91EA7D9F
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	1.fc17
PackageVersion	1.23.16
SHA-1	0BD9A500120CE08299026578724AA194E6AEF3B5
SHA-256	3023506962AC155D70A86683FE6005E1A1DD3BB2ADBB2CFA05EA85EC6A58959A

Key	Value
MD5	997E43F9C024AD9FA8064C67A53808C0
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.el4
PackageVersion	1.22.21
SHA-1	1DE046CD1C955BC052CDF0CD90B742054D1968A2
SHA-256	59C056912A0E5EF8C0F161139F4A22A6C273723019A98A90314CB3E060DE8B87

Key	Value
MD5	0D15A653CE776D1DFF48DD5F1B1DF267
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	1.fc19
PackageVersion	1.27.10
SHA-1	24A8E3A68598CAC1CA173E5D84342A69E0E8CE7A
SHA-256	069AD59DFAE9C8D100B7A13B600CD0DE8E098156DAC4A59B93E806E5713239F1

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	117D94891F9669A6C60D13D8259AA3AF
PackageArch	armv5tel
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.fc18
PackageVersion	1.25.15
SHA-1	27EB78355F26D8DA082263A46AF35F362E0B4571
SHA-256	FC8637422CDAC4E5EE36AF1255DD21F39DB324FA989A18D57E9198502C3AFB4F

Key	Value
MD5	F86AA16AC2C6AC6502ECA59BB2A9CED8
PackageArch	armv7hl
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.fc17
PackageVersion	1.23.16
SHA-1	29974D11AEAB421D5D2D16D44A608862AA96EF18
SHA-256	1C03042684FBBB1E17BA794F4603C7E70EFCF34F2556BD0DDD6A3049AFD4F646

Key	Value
FileSize	4046320
MD5	80FA07D1D375927C9BB5BA52810C2B13
PackageDescription	GNU R package for genetic marker linkage analysis R/qtl is an extensible, interactive environment for mapping quantitative trait loci (QTLs) in experimental crosses. It is implemented as an add-on-package for the freely available and widely used statistical language/software R (see http://www.r-project.org). . The development of this software as an add-on to R allows one to take advantage of the basic mathematical and statistical functions, and powerful graphics capabilities, that are provided with R. Further, the user will benefit by the seamless integration of the QTL mapping software into a general statistical analysis program. The 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. The main HMM algorithms were implemented, 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.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	r-cran-qtl
PackageSection	gnu-r
PackageVersion	1.27-10-1
SHA-1	2B998786CAEA8ACCB92EFDFC36C8979585649566
SHA-256	4245A7C59F4640269FDCA264F5434D901D6823B0CEA98F1A164049B4C7A36B3D

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	DE65889C9D59F1ACEDD3335976B94BA2
PackageArch	armv5tel
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.fc17
PackageVersion	1.23.16
SHA-1	498FD92C6A6C6A213534BDB6817A46466B24AAF5
SHA-256	B70291EF043997AE8C098FC6C1B9CFA842F3FF4E984161ACFCD01B75798A07E2