Parents (Total: 59)

The searched file hash is included in 59 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
MD5	C9F904FA1BDF623CAE8396C0749DE5FD
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, Cox model, multiple-response Gaussian, and the grouped multinomial regression. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family. This comes with a modest computational cost, so when the built-in families suffice, they should be used instead. The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers listed in the URL below.
PackageName	R-glmnet
PackageRelease	lp153.1.1
PackageVersion	4.1.3
SHA-1	27B93867AE1ECCB4D417F4517154114F35B9A824
SHA-256	6AD689DB0BE8A7A847945EDCE2B37472E1DECD397D3DB950670AEB354F56D2FB

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