| Key | Value |
|---|---|
| FileName | ./usr/lib64/R/library/glmnet/Meta/vignette.rds |
| FileSize | 257 |
| MD5 | 606D9D933BD7E4DBF80A07EC92414989 |
| SHA-1 | 49D2B43224392B8D830360C203856A3C2349BFB1 |
| SHA-256 | 71666C493D113FD5C34B40AF9B28F717E7CC1D2DC1274EEDB262025BCD6C2327 |
| SSDEEP | 6:XtQD/aNru5l4W4YBUS0Y4DwsrXOUguyNq5jePvJn5M/ukoId9ULVRpc/:X6D/dl+YS4Wwrd65iHp5ecLFc/ |
| TLSH | T1B0D0952615765C4D654617F44501F21D024377284172F774D301D7E406C40FDDCB2BC2 |
| hashlookup:parent-total | 11 |
| hashlookup:trust | 100 |
The searched file hash is included in 11 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 | 71E6911695FE5CD358381FE1837DD04E |
| 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 | lp152.1.10 |
| PackageVersion | 2.0.18 |
| SHA-1 | 014993F2B65FB9B93346002AE76A54F4289322BA |
| SHA-256 | FDA5D5E397F62BD30A61E00467B821104DA0F78AC0D3697A41307B7F56B04BED |
| Key | Value |
|---|---|
| MD5 | 913CA99019D6C21897051A4F8991C327 |
| 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 | lp151.1.41 |
| PackageVersion | 2.0.18 |
| SHA-1 | 56CD6BD10D0F9CB45C1D6C37F4D3D233E46EF875 |
| SHA-256 | C86A01688E6B2C7BB06E70980844BAD85E86EC8675BD45CC17CA7BF469198627 |
| 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 | 90F06A6E3B8B9372FB58602D8572B029 |
| 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 | lp150.1.26 |
| PackageVersion | 2.0.18 |
| SHA-1 | FF20AAA2DDA4E88A6A144191EFA122D866B6E842 |
| SHA-256 | 464A4CE55769E5D0418F36F8E528A6A09978E02940FD1318F32197A21EAE9B3E |
| 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 | F771A4532EBC20E41886CE036F74B0FF |
| 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 | lp153.1.9 |
| PackageVersion | 2.0.18 |
| SHA-1 | CD9AFF826B08965B25B59570F553D6D8E4A6E44D |
| SHA-256 | AD2C1AFDD756D15842EB8465E8AFA2623B44A9988A69097A3E48A82AD690D235 |
| 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 |