Parents (Total: 7)

The searched file hash is included in 7 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	80212388957C539523F12F93D333CDA0
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	1.48
PackageVersion	2.0.18
SHA-1	4A12F9780A2E2B83C6962797D0D50C1F08D49416
SHA-256	447C6C89B3192B7010B845219E484AE0752013ACB71139B8ADF401DEA993AD65

Key	Value
MD5	A56DDEB85D20CF4A4C3B57E7793A4751
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	1.16
PackageVersion	2.0.18
SHA-1	B92BBAE52A44F6F081719708E20F12B25188E661
SHA-256	78FA90A2A115F9EE723D58CFF00533C0CAFAEF24A9E5C8FC6C2EA09302B02A8C

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	571029F64179F5B5BB47C7F2A3F0B8EE
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	1.48
PackageVersion	2.0.18
SHA-1	3CD98CBB0B3721AD87E9E8DB05CBA41D5FD0D092
SHA-256	53C3E6F7B1E01B47B2E7519750173ED3BBD17BBB88175E0F26248D5655B666BF

Key	Value
MD5	91DA86A9B3321A44E36D5F8569466FBA
PackageArch	armv7hl
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	1.43
PackageVersion	2.0.18
SHA-1	CAEA1F34B30C6BE3823C6A47CB82E131F49AB805
SHA-256	A39B6D1C1F209934534991345E6F6B4BFC7F635A7C488F24DE1A6ADBA6CD8E8F

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	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