Parents (Total: 4)

The searched file hash is included in 4 parent files which include package known and seen by metalookup. A sample is included below:

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
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

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