Parents (Total: 46)

The searched file hash is included in 46 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	71E6911695FE5CD358381FE1837DD04E
PackageArch	x86_64
PackageDescription	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 regression. The algorithm uses cyclical coordinate descent in a path-wise fashion.
PackageName	R-glmnet
PackageRelease	lp152.1.10
PackageVersion	2.0.18
SHA-1	014993F2B65FB9B93346002AE76A54F4289322BA
SHA-256	FDA5D5E397F62BD30A61E00467B821104DA0F78AC0D3697A41307B7F56B04BED

Key	Value
FileSize	1424288
MD5	5150E710168FBA9468C4D531224D271F
PackageDescription	Lasso 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.
PackageMaintainer	Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageName	r-cran-glmnet
PackageSection	gnu-r
PackageVersion	2.0-16-2
SHA-1	06B5C2B44BC66480C4D15A7D3CA46DA0E39D4045
SHA-256	580D90130FA1CE3C9FD9E2CF04BB667E34C811B4AC6C76D9C36F96EB73BC5A17

Key	Value
FileSize	1573554
MD5	7A3291F17EC2B9EB21C413759AA8D01E
PackageDescription	Lasso 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.
PackageMaintainer	Debian Science Team <debian-science-maintainers@lists.alioth.debian.org>
PackageName	r-cran-glmnet
PackageSection	gnu-r
PackageVersion	2.0-5-1
SHA-1	08747630510E29644B3770496DAB9DE9E7B4B88A
SHA-256	361831713A49595EDB1DFCC91500952A2B7B60937609654DE093FE86F4985CF4

Key	Value
MD5	2979B186A914D0A670E7FB8FFE8E72EF
PackageArch	x86_64
PackageDescription	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 regression. The algorithm uses cyclical coordinate descent in a path-wise fashion.
PackageName	R-glmnet
PackageRelease	1.47
PackageVersion	2.0.18
SHA-1	115F2740A548A660807F9158B783AE9A9B0BCAE7
SHA-256	6E7F819C818947DC5269938EB3CD2F02105A0D66D4E2EDCBD2DA66671F08660C

Key	Value
MD5	EE0153416AAC7A8581C323493E5F5BAB
PackageArch	x86_64
PackageDescription	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 regression. The algorithm uses cyclical coordinate descent in a path-wise fashion.
PackageName	R-glmnet
PackageRelease	1.6
PackageVersion	2.0.18
SHA-1	123B4F47FFA9E4A849CD7B1B4FFE28C07A448558
SHA-256	FC94C8CF0E37BC2384EF8C32FD200D7F665A11A02D83FC6FF4F92EF442111674

Key	Value
MD5	F439485E260F07F8961D091FEEAB3175
PackageArch	x86_64
PackageDescription	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 regression. The algorithm uses cyclical coordinate descent in a path-wise fashion.
PackageName	R-glmnet
PackageRelease	1.18
PackageVersion	2.0.18
SHA-1	1AFC1147F7800566C60BF82F871662B2F92EBD20
SHA-256	4737A50A7843F82AC0AD815534E65B8DEDB782F931F96997F8841E1A2DCEE160

Key	Value
FileSize	1426652
MD5	474FE11B21DEFC08868C007ADE887FEF
PackageDescription	Lasso 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.
PackageMaintainer	Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageName	r-cran-glmnet
PackageSection	gnu-r
PackageVersion	2.0-16-2
SHA-1	227E4606FD1F2DEBA47221E35E596C409B04CFCD
SHA-256	5CFB05E5A69412AA780DC3464C922C4472C5DDD005812358DDD1F37F678427FB

Key	Value
FileSize	1563746
MD5	B127F6A6D22CD04A357A843EA999F9CA
PackageDescription	Lasso 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.
PackageMaintainer	Debian Science Team <debian-science-maintainers@lists.alioth.debian.org>
PackageName	r-cran-glmnet
PackageSection	gnu-r
PackageVersion	2.0-5-1
SHA-1	2A50FE6A35D00171A966DBBAFCE42A368A614F51
SHA-256	169DC25484E5488BF8141657149A49DCB011A8EAE2EA569886DB528C90E31930

Key	Value
FileSize	1784572
MD5	865BE0E276B721CCAB6ECAE06D652A39
PackageDescription	Lasso 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.
PackageMaintainer	Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageName	r-cran-glmnet
PackageSection	gnu-r
PackageVersion	4.1-2
SHA-1	2B9D5C7977E69C0375FBEBECC1A9D377E1048D47
SHA-256	827BE2B261B783CDF483E6B488512B1D3A9EDB35B73D592624E5C9EC94FF3547

Key	Value
FileSize	1782936
MD5	A95EC865448CF36EA5F2E44CCD3A6E70
PackageDescription	Lasso 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.
PackageMaintainer	Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageName	r-cran-glmnet
PackageSection	gnu-r
PackageVersion	4.1-2
SHA-1	2D8FF03F8FD5D29923A1236B670B33AFD7C33DD5
SHA-256	96D94CC49D85CC4D8667B8920C0F071F60F8DAE9840A76F964D4F3B5A851E6B2