Result for 02CE14B9F764E79A712F93E3AF5B9D4DF9C216A6

Query result

Key Value
FileName./usr/lib64/R/library/qtl/docs/new_summary_scantwo.R
FileSize4606
MD51E5D19906B035F068F79B3EC5562D5E9
SHA-102CE14B9F764E79A712F93E3AF5B9D4DF9C216A6
SHA-256F1555BC712862D6769F85C07E9D46F6CCD8A68C3B53BD20A326EC3DE7615DC9B
SSDEEP48:wJgyqZXnpJzesrmghKR4I1GnYxg+fk0kQlzYTOzW4uzJe6i4:fDrmqY4Lcz/zNuzv
TLSHT1FD913A4998D271E8DBDB9E20763FB2D6502DE363AE77101FA12D90A43346C33E57C242
hashlookup:parent-total21
hashlookup:trust100

Network graph view

Parents (Total: 21)

The searched file hash is included in 21 parent files which include package known and seen by metalookup. A sample is included below:

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
MD5997E43F9C024AD9FA8064C67A53808C0
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.
PackageMaintainerFedora Project
PackageNameR-qtl
PackageRelease1.el4
PackageVersion1.22.21
SHA-11DE046CD1C955BC052CDF0CD90B742054D1968A2
SHA-25659C056912A0E5EF8C0F161139F4A22A6C273723019A98A90314CB3E060DE8B87
Key Value
MD5C69799890DB8DA3AC9C9FE99EC5EC9C6
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
PackageRelease2.fc15
PackageVersion1.19.20
SHA-127375487F83723C9449849B18BBF50C94FDEB1BD
SHA-2563DCAC35AA1E67AD8D5BB42D824A0EAB44D2252345BA3A9BB938E1E1F0A747DD6
Key Value
MD5F86AA16AC2C6AC6502ECA59BB2A9CED8
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
PackageRelease1.fc17
PackageVersion1.23.16
SHA-129974D11AEAB421D5D2D16D44A608862AA96EF18
SHA-2561C03042684FBBB1E17BA794F4603C7E70EFCF34F2556BD0DDD6A3049AFD4F646
Key Value
MD5E0AA4F16E0423A2EE509FC1E7D521E97
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
PackageRelease2.fc15
PackageVersion1.19.20
SHA-12D0EF7590ECC5A0EBD66C0825E6DB99CA26418FB
SHA-256A50A809EE2AC10355976B245509A7F4C57C4174E107E159E0D42048159040CCD
Key Value
MD5DE65889C9D59F1ACEDD3335976B94BA2
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
PackageRelease1.fc17
PackageVersion1.23.16
SHA-1498FD92C6A6C6A213534BDB6817A46466B24AAF5
SHA-256B70291EF043997AE8C098FC6C1B9CFA842F3FF4E984161ACFCD01B75798A07E2
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
MD578394835F77A631626521789E6077E45
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.fc17
PackageVersion1.23.16
SHA-14C4AAF9386469EDB95143E21E23972DDA7798548
SHA-2562DDF5F54070A0D5CF92C6BE4ED34C63641C740159C0A1D3F6DF8A78585A4EA6E
Key Value
MD56181CBB002330F86AF7226A701DFD63A
PackageArchi386
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.el4
PackageVersion1.22.21
SHA-16B292A94EC1E8E142CC32971545968746BBF99E6
SHA-256D018B6BB0C34BCFFD3045413DDE76448D5055A47FFEC3EF9FAA8BF8502EB4D04
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