Result for 1428BF383BF1E92618DCF580E3785D0CEA60F417

Query result

Key Value
FileName./usr/lib64/R/library/LassoBacktracking/R/LassoBacktracking.rdb
FileSize37601
MD5680085A6FD7F206B4777185FDF91CD7B
SHA-11428BF383BF1E92618DCF580E3785D0CEA60F417
SHA-2569A8D9C1040D4B1C4D71A4C63DB5EF4060E3CF24C760DBC4771D6FE004768155A
SSDEEP768:b/zi7Q3XdzIGq3G6xtqROOXskVx/+9T9Ora7fOyCbF+txw+dNa/oEXsE/nXCYQT:b/zi7Q3Xd853G0t7OckLS9Ea7fOyCbFy
TLSHT158F2F1AF6DD050C25893B4555B14DFD0CD4804B74BE426D331BAFB8BFBAE2050EA62A9
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