Result for 6E00ABDCC6C3022E0F434D6360A8F5DFC168EA43

Query result

Key Value
FileName./usr/lib64/R/library/LassoBacktracking/R/LassoBacktracking.rdb
FileSize37606
MD51DB4070541FF558328BE0E308C320A92
SHA-16E00ABDCC6C3022E0F434D6360A8F5DFC168EA43
SHA-256DF899E1BCC651B37AB7FAD6815058402808CD697F48384959BBEB7331792FAF6
SSDEEP768:b/1+3XdzIGq3G6xtqROOXskVx/+9T9Ora7fOyCbF+txw+dNa/oEXsE/nXCYQT:b/1+3Xd853G0t7OckLS9Ea7fOyCbFMwk
TLSHT16FF2F1AE99C050C25493B5916B54CFD0CC4804F707E85AD371BAFBCBBBBE2010E9B299
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