Result for 335F0F4E597ECF93B0B46BC65BB658276DCF68F7

Query result

Key Value
FileName./usr/lib64/R/library/glmnet/R/glmnet.rdb
FileSize123426
MD5C47143FD16F49CE5C9F808F5C1981E1B
SHA-1335F0F4E597ECF93B0B46BC65BB658276DCF68F7
SHA-256E93804C112BE85DEF8D021A42A31847EA6D54201ED872DB501C5F65D3E6024FC
SSDEEP1536:z2umIy3XVOQ7MAP05SiETAfDCRJr9R2U3vX9+K6hpxOwHwBIGexEucFShaSyX0rI:5UwAs5bg2UPwK+xOw4ehvA0rnOuTs
TLSHT155C312F25D6C3E9DCFF965041983277984B90E29EB2020E30B46E4772928FF85E67225
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
MD52979B186A914D0A670E7FB8FFE8E72EF
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
PackageRelease1.47
PackageVersion2.0.18
SHA-1115F2740A548A660807F9158B783AE9A9B0BCAE7
SHA-2566E7F819C818947DC5269938EB3CD2F02105A0D66D4E2EDCBD2DA66671F08660C