Parents (Total: 13)

The searched file hash is included in 13 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
FileSize	1823580
MD5	8684180F8575B7FEFF0EE164F74F5131
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	4.1-2-1
SHA-1	CA6BC6F93824739BAC6F6AC92D43D4774F1C943F
SHA-256	100E4891B64CA7035967B0DB768909D2D3132326E4A55B23DA1E3CB68FD0A434

Key	Value
FileSize	1820660
MD5	D925D09EE1BD36FD292ADE96FE596D20
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	4.1-2-1
SHA-1	51EFA388BA795A1AB6ED154216A4606490486AA4
SHA-256	33D35E6255A7E916CEDA20D3C6669931120645A766B5C612DB0FFC503CB954D5

Key	Value
FileSize	1820004
MD5	059153F0C6A33350CD0CE7DD8FD6FB35
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	4.1-2-1
SHA-1	AFE93990F6884FC4DEB901C6E6C7644380E955FF
SHA-256	3A1FD88E778DC13B3150B1E2EC8D36A7A036EC60B488B73C1000FABFE9238430

Key	Value
MD5	9362F3D01DDAD6F1278F63A06189162C
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	lp154.1.1
PackageVersion	4.1.3
SHA-1	CF8180F8BA52271E4C1D365EC1F4970D6A97F473
SHA-256	86535A789AF3A8034F8731316993DCC9EC45F3FD2876CA539B8F2520FF68F7B9

Key	Value
FileSize	1841008
MD5	BF96AFC583B92E7920387F033CC07223
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	4.1-2-1
SHA-1	F6363A1D5FA04FA00BA7C826A3DF6D4E8D99C499
SHA-256	8EAEEFAEF6186D245A3ED29268276CFFB2347AC4095E7AAEB1AAC21215740AE7

Key	Value
FileSize	1809332
MD5	07793E4A27D9ED2FF09BEFD737C9D207
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	4.1-2-1
SHA-1	6B706090536E321533ED8FF3957B4C575E0B4347
SHA-256	99C4E5F57E03A1C8736D254998F9FC53E287849526D0FE625B24B69EE93BA2AC

Key	Value
FileSize	1815844
MD5	F889720F655546A5488712542B6E91C8
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	4.1-2-1
SHA-1	A32CA3CB6820F38D76720435066EC753D510C6CA
SHA-256	E957A5530FD468EF41B3657857769C056710FA3B0EDA34D8F2419DEE175F72F0

Key	Value
FileSize	1829724
MD5	6C6F64DD34E1F17F3488F2A3A8189436
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	4.1-2-1
SHA-1	B64CAC41CF22E41E7AA951BB319333CE2950781B
SHA-256	A6831016F41F43EA641B73696A36E7808792F710E018279ACF0D4EBE6DAC1477

Key	Value
MD5	92B92894E5880A87FA6A1A4683E6EFC5
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	lp152.1.1
PackageVersion	4.1.3
SHA-1	957C313036C1A6868DBD28F221D88CC133233C79
SHA-256	A99F3D285491117F397E34F572BE4F442904A8D704BF057FB66AF076B7AC1B37

Key	Value
FileSize	1823076
MD5	8A4E0D11AF1BC692F658C346E9521829
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	4.1-2-1
SHA-1	E17612D08955110E0616F76B2C032F2ACFFA5CF6
SHA-256	6EE53E0A5CE77D313BDEAC2A519D6482E65B327381C509CF2BE5B807AF132EB5

Key	Value
FileSize	1816124
MD5	8F82F0B43E7B3D6D2E8D1999FF431900
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	4.1-2-1
SHA-1	C63C7AB4D2D4CF579D28C096AF375E4A0CDA5EE8
SHA-256	04FE41B8B533A6FFD84BFCB82062582BD03C9316BAF4F0E2584BBD8B701589C1

Key	Value
MD5	273703A00DF54B82A21C4D6D80979CC5
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	1.5
PackageVersion	4.1.3
SHA-1	61C617CA894F98F158A30F108955341F359D5E4A
SHA-256	C588AEE5F3B23D51D47D1B9C9AC45DB8F69A3BD989B8045A504852B3A9285B1C

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