Result for 2987FD5A46B94E024A2EED87075818EA4B1BD3DB

Query result

Key Value
FileName./usr/lib64/R/library/glmnet/help/glmnet.rdb
FileSize59038
MD5BBDA16BC58986175C745A5A74387B8A4
SHA-12987FD5A46B94E024A2EED87075818EA4B1BD3DB
SHA-2563A7D20B6BE7CEDEFFF6C5F6013E3F6B93579D16A71885E11898B5E88BAD60721
SSDEEP1536:SihH1d10boCPeL0cX2dUo5b4sLlT3ufeqDwGwSTyONW4vIopVl:FXd6lPatmh5b/L93EetWWYWSl
TLSHT11F43F1DCC524A10845A88E194CA15E2424FF9055DDEABECFCD701BDE8ED3CE6F05AAD8
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
MD5913CA99019D6C21897051A4F8991C327
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
PackageReleaselp151.1.41
PackageVersion2.0.18
SHA-156CD6BD10D0F9CB45C1D6C37F4D3D233E46EF875
SHA-256C86A01688E6B2C7BB06E70980844BAD85E86EC8675BD45CC17CA7BF469198627
Key Value
MD571E6911695FE5CD358381FE1837DD04E
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
PackageReleaselp152.1.10
PackageVersion2.0.18
SHA-1014993F2B65FB9B93346002AE76A54F4289322BA
SHA-256FDA5D5E397F62BD30A61E00467B821104DA0F78AC0D3697A41307B7F56B04BED
Key Value
MD590F06A6E3B8B9372FB58602D8572B029
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
PackageReleaselp150.1.26
PackageVersion2.0.18
SHA-1FF20AAA2DDA4E88A6A144191EFA122D866B6E842
SHA-256464A4CE55769E5D0418F36F8E528A6A09978E02940FD1318F32197A21EAE9B3E
Key Value
MD5F771A4532EBC20E41886CE036F74B0FF
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
PackageReleaselp153.1.9
PackageVersion2.0.18
SHA-1CD9AFF826B08965B25B59570F553D6D8E4A6E44D
SHA-256AD2C1AFDD756D15842EB8465E8AFA2623B44A9988A69097A3E48A82AD690D235