Result for 4FCE3A38B2ECF4ED5EF94E2174DDC953B7B7267A

Query result

Key Value
FileName./usr/lib64/R/library/LassoBacktracking/DESCRIPTION
FileSize1097
MD52F758615887A32C508E07A3FAA09F333
SHA-14FCE3A38B2ECF4ED5EF94E2174DDC953B7B7267A
SHA-2564249C505CDBFE37FF8DB7D51D15ECB0061E610493240953191388C4AC8702F2E
SSDEEP24:HQWAw8eeqbKM1qmLBkfELd7QTB8Q1lUOnLVwWfedAnfQQlu7oEu31rGG:rx8tqbKMhBkc5G2Q1lUOnLVBWqfQKt8G
TLSHT1131123105CC073B96E4A5B3BAFB50340A728623A31F04495BD1D97681F09A6AA6A3718
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
MD5E20B4C5C1810350DBCDA76093FE81EAE
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.
PackageNameR-LassoBacktracking
PackageReleaselp153.2.3
PackageVersion0.1.2
SHA-11D1744933B95A664F1CC8EA75C6AC813FFBF0CCB
SHA-2563874480241DFDB17B2B196E6886407108AAB42EBBC14F868E9CA4E2E37E12AA5