Result for 45B10D05281DA0EF28BAB2E33638849E68C40189

Query result

Key Value
FileName./usr/lib64/R/library/LassoBacktracking/DESCRIPTION
FileSize1097
MD5EF1CBA8DDF13A5C2CCE21F95723BDDEC
SHA-145B10D05281DA0EF28BAB2E33638849E68C40189
SHA-2567F835EF9B5386B600CB50C92FB8C3153905004C00822AF1B7169F5F8BC484A90
SSDEEP24:HQWAw8eeqbKM1qmLBkfELd7QTB8Q1lUOnLVwWfedAnfQQlu7oEu31rGnO:rx8tqbKMhBkc5G2Q1lUOnLVBWqfQKt8O
TLSHT1B31146105CC073B97E8A5B3B6FF50340B72C623A31F04495BD1D93681F09A6AAAB3718
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
MD5A47CAA386B83042C17B31B428C2229F5
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
PackageRelease2.28
PackageVersion0.1.2
SHA-1850558920D6912397EA3185CEAE81CA0D908E930
SHA-256CFDF4B3E9D9FE67F7C8C988F9444B622A0D6919ED301C70A7B1DE271CC4EFE92