Result for 01E839E715B822DC6149FAF3D038EB36D174987B

Query result

Key Value
FileName./usr/lib/R/site-library/glmnet/doc/glmnet_beta.Rmd
FileSize52036
MD599C32CE3AC28A16FCE07AC7727B6AC96
SHA-101E839E715B822DC6149FAF3D038EB36D174987B
SHA-256477F0F3657211986B2AB8AA3620E84AE5031EC6BEE69F367D031C6A053AAD180
SSDEEP1536:6J1+9bbcuzLApdXIsANqQfDGk6cZUgztOykeK:K+FwuzLApdXIBNffX6Ui
TLSHT1C333F977B34D23B21B5301F5D74F01E5BB2981F8B3A289D4346E87281606E6DA2BB7D4
hashlookup:parent-total10
hashlookup:trust100

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Parents (Total: 10)

The searched file hash is included in 10 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize1560496
MD549E54FBCCC9E416B3A765468FC8C2FCF
PackageDescriptionLasso and Elastic-Net Regularized Generalized Linear Models Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper Introduction to Glmnet.
PackageMaintainerDebian Science Team <debian-science-maintainers@lists.alioth.debian.org>
PackageNamer-cran-glmnet
PackageSectiongnu-r
PackageVersion2.0-5-1
SHA-16A3B8ABB60F740F63CD86B0A2A002759FFD5612C
SHA-25604203B968767E1DE11DF7CB19289CB3110E7D32B887AA047DDC3752F947C76D1
Key Value
FileSize1577852
MD5521E25BADFAC5BE24E54CCF1189A5BE2
PackageDescriptionLasso and Elastic-Net Regularized Generalized Linear Models Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper Introduction to Glmnet.
PackageMaintainerDebian Science Team <debian-science-maintainers@lists.alioth.debian.org>
PackageNamer-cran-glmnet
PackageSectiongnu-r
PackageVersion2.0-5-1
SHA-1E730C89B15F19CEA80BA5DBC34AEFF060AAFC239
SHA-25681273244099AF043D34410509E42535C19635DFFBFAC9E5C43D1C6760026F8AD
Key Value
FileSize1572724
MD53B2B7AF3464C868CB02B24ED6FF50CBE
PackageDescriptionLasso and Elastic-Net Regularized Generalized Linear Models Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper Introduction to Glmnet.
PackageMaintainerDebian Science Team <debian-science-maintainers@lists.alioth.debian.org>
PackageNamer-cran-glmnet
PackageSectiongnu-r
PackageVersion2.0-5-1
SHA-16BADA695A1CC8225D91069023183F2DB6C133371
SHA-256D32C4E1955DF7ECBF6106A6D80B46D33BF9FAB3FC781241A4CCA800FB1C49ECC
Key Value
FileSize1573554
MD57A3291F17EC2B9EB21C413759AA8D01E
PackageDescriptionLasso and Elastic-Net Regularized Generalized Linear Models Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper Introduction to Glmnet.
PackageMaintainerDebian Science Team <debian-science-maintainers@lists.alioth.debian.org>
PackageNamer-cran-glmnet
PackageSectiongnu-r
PackageVersion2.0-5-1
SHA-108747630510E29644B3770496DAB9DE9E7B4B88A
SHA-256361831713A49595EDB1DFCC91500952A2B7B60937609654DE093FE86F4985CF4
Key Value
FileSize1582602
MD5731240A30DFC687E31B04A607D37D363
PackageDescriptionLasso and Elastic-Net Regularized Generalized Linear Models Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper Introduction to Glmnet.
PackageMaintainerDebian Science Team <debian-science-maintainers@lists.alioth.debian.org>
PackageNamer-cran-glmnet
PackageSectiongnu-r
PackageVersion2.0-5-1
SHA-1F88864093E0183332979F8E2EB0BE8C8BD225790
SHA-256590E0F410FEFB758A4BFB2B45309A85C51414DCFA58C4843898A79054A904467
Key Value
FileSize1573324
MD53CFDBA7DE5B7FFE3D0AF9A08F4EE70CF
PackageDescriptionLasso and Elastic-Net Regularized Generalized Linear Models Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper Introduction to Glmnet.
PackageMaintainerDebian Science Team <debian-science-maintainers@lists.alioth.debian.org>
PackageNamer-cran-glmnet
PackageSectiongnu-r
PackageVersion2.0-5-1
SHA-1DE55CF707F0A1260DE162CED023858D16EAF3836
SHA-256ED61A077F6F95DB76C807A121AD722EC6A9D4268330DF4CFE59307B78371D414
Key Value
FileSize1566658
MD529FD6BEA34DC5DF4E73BC0D4A15B96C2
PackageDescriptionLasso and Elastic-Net Regularized Generalized Linear Models Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper Introduction to Glmnet.
PackageMaintainerDebian Science Team <debian-science-maintainers@lists.alioth.debian.org>
PackageNamer-cran-glmnet
PackageSectiongnu-r
PackageVersion2.0-5-1
SHA-1F2E822977DBCB21BF5F8E2A66899C8057168496B
SHA-256924BE32B3D19EE1144394E4F4705C401039BB5413935E64D889B17F471C70A17
Key Value
FileSize1563746
MD5B127F6A6D22CD04A357A843EA999F9CA
PackageDescriptionLasso and Elastic-Net Regularized Generalized Linear Models Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper Introduction to Glmnet.
PackageMaintainerDebian Science Team <debian-science-maintainers@lists.alioth.debian.org>
PackageNamer-cran-glmnet
PackageSectiongnu-r
PackageVersion2.0-5-1
SHA-12A50FE6A35D00171A966DBBAFCE42A368A614F51
SHA-256169DC25484E5488BF8141657149A49DCB011A8EAE2EA569886DB528C90E31930
Key Value
FileSize1566690
MD56B89D5849E41A7A7FCAF949DD2AC5999
PackageDescriptionLasso and Elastic-Net Regularized Generalized Linear Models Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper Introduction to Glmnet.
PackageMaintainerDebian Science Team <debian-science-maintainers@lists.alioth.debian.org>
PackageNamer-cran-glmnet
PackageSectiongnu-r
PackageVersion2.0-5-1
SHA-179AFFC077EA752B7F1B411A9BDEBDB7486135454
SHA-25699C4200BEC499A63637C181731F2044F6A3D0A35F770E39C244780281FA78A2C
Key Value
FileSize1571342
MD5AE2F30DA386F7A7261D94DCAC066A0C9
PackageDescriptionLasso and Elastic-Net Regularized Generalized Linear Models Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper Introduction to Glmnet.
PackageMaintainerDebian Science Team <debian-science-maintainers@lists.alioth.debian.org>
PackageNamer-cran-glmnet
PackageSectiongnu-r
PackageVersion2.0-5-1
SHA-1B0AA6988674CB6DB150CBE5AEFEA0F06EF22F96A
SHA-2564424FA114A7CFCF51ADE5B69FA2C7B18D445AA8E9BA0814A514BB77439FB21EE