Result for 0A07721B2E56797B5CF5133485CA4DEDCB6D89EA

Query result

Key Value
FileName./usr/lib64/R/library/glmnet/R/glmnet.rdx
FileSize1970
MD510ECDB12E33DDDEAB866C591170070E0
SHA-10A07721B2E56797B5CF5133485CA4DEDCB6D89EA
SHA-256D2DB33B86C948FF62890178A26DFFA98AED9D35B2E40498FC856B36F5C2357BC
SSDEEP48:XmWCwW5EXee+n8FTP3cBzfI0pyPbsaqizl+yI/PiljSk:WnoWSTPs5fI5Po/2QJqlGk
TLSHT10E410A064241A02EB998A07765DD7F081DF3D4888699ECFEED20F294C4031720AFB738
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
MD592B92894E5880A87FA6A1A4683E6EFC5
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
PackageReleaselp152.1.1
PackageVersion4.1.3
SHA-1957C313036C1A6868DBD28F221D88CC133233C79
SHA-256A99F3D285491117F397E34F572BE4F442904A8D704BF057FB66AF076B7AC1B37