Result for 5D8B1EFA4E5FF215B12F420417DAE2D82E836B8C

Query result

Key Value
FileName./usr/lib64/R/library/LassoBacktracking/DESCRIPTION
FileSize1097
MD50F0C77159FEF441EB0A24A6F30FBCE6D
SHA-15D8B1EFA4E5FF215B12F420417DAE2D82E836B8C
SHA-25679BDC1340FDF37450A4C81B7CF91BA0B10DB1D486236636B628D4A25BDDF4A6C
SSDEEP24:HQWAw8eeqbKM1qmLBkfELd7QTB8Q1lUOnLVwWfedAnfQQlu7oEu31rGkwn:rx8tqbKMhBkc5G2Q1lUOnLVBWqfQKt8t
TLSHT17B1146105CC073B97E8A5B3B6FB50340B72C623E31F04495BD1D53681F09A6AA7B3718
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
MD596F101A9268EC58EDF785558BA149048
PackageArchx86_64
PackageDescriptionImplementation of the algorithm introduced in Shah, R. D. (2016) <http://www.jmlr.org/papers/volume17/13-515/13-515.pdf>. Data with thousands of predictors can be handled. The algorithm performs sequential Lasso fits on design matrices containing increasing sets of candidate interactions. Previous fits are used to greatly speed up subsequent fits so the algorithm is very efficient.
PackageMaintainerhttps://www.suse.com/
PackageNameR-LassoBacktracking
PackageReleaselp154.2.1
PackageVersion0.1.2
SHA-100098FB3933A291D2F4962355806959F77BE7F37
SHA-256FC538C14B1F6B3ACD1229797AF7E7E6C258E52476758EA74BC4C3FCFA39A3858