Parents (Total: 4)
The searched file hash is included in 4 parent files which include package known and seen by metalookup. A sample is included below:
| Key |
Value |
| MD5 | E20B4C5C1810350DBCDA76093FE81EAE |
| PackageArch | x86_64 |
| PackageDescription | Implementation 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. |
| PackageName | R-LassoBacktracking |
| PackageRelease | lp153.2.3 |
| PackageVersion | 0.1.2 |
| SHA-1 | 1D1744933B95A664F1CC8EA75C6AC813FFBF0CCB |
| SHA-256 | 3874480241DFDB17B2B196E6886407108AAB42EBBC14F868E9CA4E2E37E12AA5 |
| Key |
Value |
| MD5 | 4B7E5A3D7C1125D1A6665AB402C3D7B8 |
| PackageArch | x86_64 |
| PackageDescription | Implementation 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. |
| PackageName | R-LassoBacktracking |
| PackageRelease | lp152.2.7 |
| PackageVersion | 0.1.2 |
| SHA-1 | 7D34061EBCA47D2070E1AC3A318B627107238A1A |
| SHA-256 | 0B09474CD8B88450F64F2C1FDFCE0320EC7D8F549B995B0FD45E6C9AC73D63A2 |
| Key |
Value |
| MD5 | A47CAA386B83042C17B31B428C2229F5 |
| PackageArch | x86_64 |
| PackageDescription | Implementation 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. |
| PackageName | R-LassoBacktracking |
| PackageRelease | 2.28 |
| PackageVersion | 0.1.2 |
| SHA-1 | 850558920D6912397EA3185CEAE81CA0D908E930 |
| SHA-256 | CFDF4B3E9D9FE67F7C8C988F9444B622A0D6919ED301C70A7B1DE271CC4EFE92 |
| Key |
Value |
| MD5 | 96F101A9268EC58EDF785558BA149048 |
| PackageArch | x86_64 |
| PackageDescription | Implementation 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. |
| PackageMaintainer | https://www.suse.com/ |
| PackageName | R-LassoBacktracking |
| PackageRelease | lp154.2.1 |
| PackageVersion | 0.1.2 |
| SHA-1 | 00098FB3933A291D2F4962355806959F77BE7F37 |
| SHA-256 | FC538C14B1F6B3ACD1229797AF7E7E6C258E52476758EA74BC4C3FCFA39A3858 |