Result for 17EBA9948297A48AF8D433A90735E8E9C50F080A

Query result

Key Value
FileName./usr/lib64/R/library/glmnet/R/glmnet.rdb
FileSize287606
MD514D8D36510749AA34C65CA45F15F45D0
SHA-117EBA9948297A48AF8D433A90735E8E9C50F080A
SHA-2565CAAB4A2487C292DC1DECF03212E0C1BE987153710074AF14B72A5935EDB2FAC
SSDEEP6144:v1h6ZdkQ+SGS5lDq4R8wRYUe85AB0zfGGvAc3z:T6ZdnBG6lt8g5AuGq3z
TLSHT14C5423C715E9C298503A30C8339F1BB2D407CFFEF1CC82126555DA6A46B6B99836B76C
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD59362F3D01DDAD6F1278F63A06189162C
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, Cox model, multiple-response Gaussian, and the grouped multinomial regression. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family. This comes with a modest computational cost, so when the built-in families suffice, they should be used instead. The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers listed in the URL below.
PackageNameR-glmnet
PackageReleaselp154.1.1
PackageVersion4.1.3
SHA-1CF8180F8BA52271E4C1D365EC1F4970D6A97F473
SHA-25686535A789AF3A8034F8731316993DCC9EC45F3FD2876CA539B8F2520FF68F7B9