Result for 13750EF442E3237485096FE03BE8F9FE71E33ED5

Query result

Key Value
FileName./usr/lib/R/site-library/qtl/sampledata/listeria.csv
FileSize34949
MD59198BD35E7A221556C5E2008D00CF84C
SHA-113750EF442E3237485096FE03BE8F9FE71E33ED5
SHA-256024931DA3FC64D101F4CF3AC386E576661618BD74CD7B7341EDC1AF87CB5E749
SSDEEP96:G4DKhv777777777756QLMOzvHYe2QRE6+yS1Hxog0BDcYj0z0Iby29S9SvZzX2TT:GUY7777777777xL5IZoUcTMG
TLSHT14BF22EDA43DBCBB153FC0187A20C2DB8B18EE8B734691184D60B545A90F8DE575B3ADB
hashlookup:parent-total148
hashlookup:trust100

Network graph view

Parents (Total: 148)

The searched file hash is included in 148 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize4202324
MD599686B1BD763235E671CBBB2E30D1BBE
PackageDescriptionGNU 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, with allowance for the presence of genotyping errors, for backcrosses, intercrosses, and phase-known four-way crosses were implemented. . 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.
PackageMaintainerDebian Med Packaging Team <debian-med-packaging@lists.alioth.debian.org>
PackageNamer-cran-qtl
PackageSectiongnu-r
PackageVersion1.40-8-1
SHA-1012F6E3BB53279E9B6AABD81E2BC50BBF1E2FF8F
SHA-256660B67D4C1B29086E127E03045A7292F1AA49F0B1B72BF9DF2DCB973D7B813FD
Key Value
MD5B639DFFE61E1BA6AA9050B098A25F638
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
PackageRelease1.el8
PackageVersion1.46.2
SHA-10328667C94A7E2301C3B51FE383929AAA7246045
SHA-2561BB737356F4E368E1A83ACBDA7A76A47119BB47F30210903C2F44537DB537D02
Key Value
MD57F0EAE861B694463414031C1E3A0D919
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
PackageRelease1.fc34
PackageVersion1.48.1
SHA-104395CD09440B0BCCC7F0934EDFD8791E3FC2B2D
SHA-2565BBDAF293CDA5CF7AEA02B07FDD7B8D7059477AC542276CE0AAA5C25163AE21B
Key Value
FileSize4076788
MD55DC9592A5A297E575744E0E854D23702
PackageDescriptionGNU 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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamer-cran-qtl
PackageSectiongnu-r
PackageVersion1.27-10-1
SHA-104B33AD1689247407376A95D5A325F07047C266C
SHA-25693CF1870DE7169A8D627DCE20994EF6DD138F0C8F86D1ED764A1D3570B3D9921
Key Value
FileSize5022684
MD5994F37C529B630C00FF8CE7C540C28B5
PackageDescriptionGNU 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, with allowance for the presence of genotyping errors, for backcrosses, intercrosses, and phase-known four-way crosses were implemented. . 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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamer-cran-qtl
PackageSectiongnu-r
PackageVersion1.37-11-1
SHA-10765EF32CCED8DFFBE9D15F85B426C96ACC089CF
SHA-25656C37B4EE4F9A10B11445CCD5433525A616C972DEC9552E2E31A1AB067B3D457
Key Value
MD58B59A3225F63F316459C8D7A91EA7D9F
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.fc17
PackageVersion1.23.16
SHA-10BD9A500120CE08299026578724AA194E6AEF3B5
SHA-2563023506962AC155D70A86683FE6005E1A1DD3BB2ADBB2CFA05EA85EC6A58959A
Key Value
MD53CAE8DDBDD3C8C530F738A2077465F2E
PackageArchppc64le
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.el8
PackageVersion1.46.2
SHA-10D5F3B4D956E5DFB36F8A429B1A55CA830FB5F9D
SHA-25693964834B8DA052B4E3DF741CCD37F9DE5666728633F565476D7E23DFE0EC66A
Key Value
FileSize5512124
MD5C300B6A622E3E70B72373E196A71AE51
PackageDescriptionGNU 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, with allowance for the presence of genotyping errors, for backcrosses, intercrosses, and phase-known four-way crosses were implemented. . 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.
PackageMaintainerDebian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageNamer-cran-qtl
PackageSectiongnu-r
PackageVersion1.44-9-1
SHA-10E17D939F22C3DB6A41069624595EF4204B80D4B
SHA-2561BA3FC9194A6409F75539F371E21FBF8C55A8543C1B363371F5D19FE637C475E
Key Value
MD57E11E63E211156249326E0E8EC466F4D
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.
PackageMaintainerFedora Project
PackageNameR-qtl
PackageRelease1.fc20
PackageVersion1.28.19
SHA-10EC3C659ABDD78EFEF862C7193B80B5D080DF978
SHA-256255BE44F551362192BE44CDB096023CF7B2098E57104F23A5660848A5F6C619A
Key Value
FileSize5515160
MD5C3DC3AF48D24EE23D47621165BAE2D05
PackageDescriptionGNU 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, with allowance for the presence of genotyping errors, for backcrosses, intercrosses, and phase-known four-way crosses were implemented. . 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.
PackageMaintainerDebian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageNamer-cran-qtl
PackageSectiongnu-r
PackageVersion1.50-1
SHA-114243A7D65A38B9D9FDD01FE23CA8198E37E7194
SHA-256D384B40D18182758C1229BC6FF90481DA48F8755E1DEA9D5A3A68DAAC3B3AFB5