Result for 50EBA139B0EE172948E82A16E88C51D300120EA3

Query result

Key Value
FileName./usr/lib64/R/library/LassoBacktracking/R/LassoBacktracking.rdb
FileSize37606
MD5508F9320AAD21E3C8F5C9C38A71D7A60
SHA-150EBA139B0EE172948E82A16E88C51D300120EA3
SHA-256CBF84481F3DFA6CC96F5F033E903491EFABAE257EF3A873E6FA7A5A7B2462B51
SSDEEP768:b/Y1mpZRy53XdzIGq3G6xtqROOXskVx/+9T9Ora7fOyCbF+txw+dNa/oEXsE/nXO:b/YUpZRS3Xd853G0t7OckLS9Ea7fOyCk
TLSHT154F2F1AEA9D064C24893B5515B24DFE1CD4804B31BE496C371BAFB8BFBDE1010D972A9
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
MD54B7E5A3D7C1125D1A6665AB402C3D7B8
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
PackageReleaselp152.2.7
PackageVersion0.1.2
SHA-17D34061EBCA47D2070E1AC3A318B627107238A1A
SHA-2560B09474CD8B88450F64F2C1FDFCE0320EC7D8F549B995B0FD45E6C9AC73D63A2