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	D00ED811A2D5223B90CF4FB97D1BB0CE
PackageArch	x86_64
PackageDescription	The fast cross-validation via sequential testing (CVST) procedure is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating under-performing candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.
PackageName	R-CVST
PackageRelease	lp153.2.3
PackageVersion	0.2.2
SHA-1	EE84C0AEF87804DA73D07E76C76301F4A3A43C15
SHA-256	746FFA0B518F011AB116100EF7303D50FB4DFBEE8308C2EBF06A1D6CC453CAB6

Key	Value
MD5	AE9C79468CD273777FCB82DDAEEE51F3
PackageArch	x86_64
PackageDescription	The fast cross-validation via sequential testing (CVST) procedure is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating under-performing candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.
PackageName	R-CVST
PackageRelease	lp152.2.6
PackageVersion	0.2.2
SHA-1	267F3C83EE5D8ADB2F413D8CF06C74B31EE72453
SHA-256	0EFFB06E84B4E5DBAC6D1E9174788933F67C38E779950BB0C30A43A811A9655D

Key	Value
MD5	3B4FF4E1584D374A1D5ABA8E6B3CE121
PackageArch	x86_64
PackageDescription	The fast cross-validation via sequential testing (CVST) procedure is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating under-performing candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.
PackageName	R-CVST
PackageRelease	2.24
PackageVersion	0.2.2
SHA-1	F61C4BBC76BFDE951A00350BB66FE1685887C361
SHA-256	083709A5528E2AB0A0039A4C1D4F4E54ABF4C5728ECBD6835B6ED54506C5A702

Key	Value
MD5	505A926DEEF0EE159CB61F00AB9BFE5B
PackageArch	x86_64
PackageDescription	The fast cross-validation via sequential testing (CVST) procedure is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating under-performing candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.
PackageMaintainer	https://www.suse.com/
PackageName	R-CVST
PackageRelease	lp154.2.1
PackageVersion	0.2.2
SHA-1	4FC0E11AFD02F976096882950C38A21E0F76B795
SHA-256	3194154DF768C67C120E5EEA066E6D53FA581015429CA2E70A1F1918FE1176B7