Key | Value |
---|---|
FileName | ./usr/lib64/R/library/glmnet/help/glmnet.rdb |
FileSize | 59038 |
MD5 | BBDA16BC58986175C745A5A74387B8A4 |
SHA-1 | 2987FD5A46B94E024A2EED87075818EA4B1BD3DB |
SHA-256 | 3A7D20B6BE7CEDEFFF6C5F6013E3F6B93579D16A71885E11898B5E88BAD60721 |
SSDEEP | 1536:SihH1d10boCPeL0cX2dUo5b4sLlT3ufeqDwGwSTyONW4vIopVl:FXd6lPatmh5b/L93EetWWYWSl |
TLSH | T11F43F1DCC524A10845A88E194CA15E2424FF9055DDEABECFCD701BDE8ED3CE6F05AAD8 |
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 | 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 | 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 | 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 | 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 |