Result for 1715503091414CEB8500A00E18CC762382683658

Query result

Key Value
FileName./usr/lib64/R/library/glmnet/R/glmnet.rdx
FileSize986
MD5E7F622C6ADC0D5DF029F11151F189A65
SHA-11715503091414CEB8500A00E18CC762382683658
SHA-256F55536B3B6C1C8BBEF7C396511A542AFE9BE5E3B92BCAFCFFB0B88813206F009
SSDEEP24:Xp3JawGdpQ0qZ8uIjKt86j7ZIw462yKOYmIoMGtM:Xp53GdcIKhXGyV/rIojM
TLSHT1D511C848CC905E5B1408EF6F91A727039ADD03228272FAC680F411BD9020DF102FDD08
hashlookup:parent-total4
hashlookup:trust70

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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
MD5571029F64179F5B5BB47C7F2A3F0B8EE
PackageArchx86_64
PackageDescriptionExtremely 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.
PackageNameR-glmnet
PackageRelease1.48
PackageVersion2.0.18
SHA-13CD98CBB0B3721AD87E9E8DB05CBA41D5FD0D092
SHA-25653C3E6F7B1E01B47B2E7519750173ED3BBD17BBB88175E0F26248D5655B666BF
Key Value
MD5F439485E260F07F8961D091FEEAB3175
PackageArchx86_64
PackageDescriptionExtremely 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.
PackageNameR-glmnet
PackageRelease1.18
PackageVersion2.0.18
SHA-11AFC1147F7800566C60BF82F871662B2F92EBD20
SHA-2564737A50A7843F82AC0AD815534E65B8DEDB782F931F96997F8841E1A2DCEE160
Key Value
MD5EE0153416AAC7A8581C323493E5F5BAB
PackageArchx86_64
PackageDescriptionExtremely 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.
PackageNameR-glmnet
PackageRelease1.6
PackageVersion2.0.18
SHA-1123B4F47FFA9E4A849CD7B1B4FFE28C07A448558
SHA-256FC94C8CF0E37BC2384EF8C32FD200D7F665A11A02D83FC6FF4F92EF442111674
Key Value
MD580212388957C539523F12F93D333CDA0
PackageArchx86_64
PackageDescriptionExtremely 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.
PackageNameR-glmnet
PackageRelease1.48
PackageVersion2.0.18
SHA-14A12F9780A2E2B83C6962797D0D50C1F08D49416
SHA-256447C6C89B3192B7010B845219E484AE0752013ACB71139B8ADF401DEA993AD65