Result for 15F395D3EF917D63F52EBF71DA91C0E7A79721E5

Query result

Key Value
FileName./usr/lib64/R/library/glmnet/DESCRIPTION
FileSize2168
MD58797EBBE67AB70DF8FC9AC2277328B6A
SHA-115F395D3EF917D63F52EBF71DA91C0E7A79721E5
SHA-256B9B531F5E3EE5628562BAEA395E403E34D67ED00382EB9E72D27D58AEC4A6F97
SSDEEP48:C/+yqQOtn7mudVckDXp/P+/S1GXidOOX/PUKFIjzdONwn3:C/+wi9X+SGXiMOX3ozdOg3
TLSHT1034195034A481627378A435B3C380792FB22C32C76A254A1BDED5F3C270C9BA47BE780
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
MD5C9F904FA1BDF623CAE8396C0749DE5FD
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
PackageReleaselp153.1.1
PackageVersion4.1.3
SHA-127B93867AE1ECCB4D417F4517154114F35B9A824
SHA-2566AD689DB0BE8A7A847945EDCE2B37472E1DECD397D3DB950670AEB354F56D2FB