Result for 39A223A04AE7AFAFE4DF32CD71BFE763CBFC46C3

Query result

Key Value
FileName./usr/lib64/R/library/CVST/R/CVST.rdb
FileSize46642
MD55F94101AD40E5E21DEC137B6BE967E1C
SHA-139A223A04AE7AFAFE4DF32CD71BFE763CBFC46C3
SHA-25699C8101DE87B867EE700AD8CF96651D853958531CC7B146592F64D5B91D4F3D3
SSDEEP768:zofqc3X9k/RtIi1r9ASKqpomiPbZImtEshKbtBFZPo6Qp/xTrCA9Du1XiEs6mfrj:zoV3Xa/jx9ASKqprI9htNIP6/oA9C1bc
TLSHT1D123F24EAFC76A37F311854E7661CC69B9866CBCF2C8C684A2A2C506E8DD65E0DD31C1
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
MD5D00ED811A2D5223B90CF4FB97D1BB0CE
PackageArchx86_64
PackageDescriptionThe 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.
PackageNameR-CVST
PackageReleaselp153.2.3
PackageVersion0.2.2
SHA-1EE84C0AEF87804DA73D07E76C76301F4A3A43C15
SHA-256746FFA0B518F011AB116100EF7303D50FB4DFBEE8308C2EBF06A1D6CC453CAB6