Key | Value |
---|---|
FileName | ./usr/lib64/R/library/glmnet/R/glmnet.rdx |
FileSize | 986 |
MD5 | E7F622C6ADC0D5DF029F11151F189A65 |
SHA-1 | 1715503091414CEB8500A00E18CC762382683658 |
SHA-256 | F55536B3B6C1C8BBEF7C396511A542AFE9BE5E3B92BCAFCFFB0B88813206F009 |
SSDEEP | 24:Xp3JawGdpQ0qZ8uIjKt86j7ZIw462yKOYmIoMGtM:Xp53GdcIKhXGyV/rIojM |
TLSH | T1D511C848CC905E5B1408EF6F91A727039ADD03228272FAC680F411BD9020DF102FDD08 |
hashlookup:parent-total | 4 |
hashlookup:trust | 70 |
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 | 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 | 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 |
---|---|
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 | 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 |