Result for 0DD0F6A109BC353073EDB3C7798145DA74A3816E

Query result

Key Value
FileName./usr/lib/R/site-library/party/help/paths.rds
FileSize382
MD5DA851B7C7AB8C70684ED8DF67D05FAE3
SHA-10DD0F6A109BC353073EDB3C7798145DA74A3816E
SHA-256AE3F4752429182C5482371003FDCA1A3F4CC860F617742C20F87F01BF2ECA413
SSDEEP6:XtV9axgjJ0km8dw9ZiU783kBXqrYU/aijcRRbQz8O9zfdZv31mqSpMZVEcirL8pj:XdyefHkZPmFpTwRRb5O9n3Ulgirpy
TLSHT176E0F101442B59A4C8594010C1DDC0C35C533BE84988DB828868216337E9F42B3CB5A4
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
FileSize1121744
MD58F3ED2CACAA9D148C17A011465D73C07
PackageDescriptionGNU R laboratory for recursive partytioning A computational toolbox for recursive partitioning. The core of the package is ctree(), an implementation of conditional inference trees which embed tree-structured regression models into a well defined theory of conditional inference procedures. This non-parametric class of regression trees is applicable to all kinds of regression problems, including nominal, ordinal, numeric, censored as well as multivariate response variables and arbitrary measurement scales of the covariates. Based on conditional inference trees, cforest() provides an implementation of Breiman's random forests. The function mob() implements an algorithm for recursive partitioning based on parametric models (e.g. linear models, GLMs or survival regression) employing parameter instability tests for split selection. Extensible functionality for visualizing tree-structured regression models is available. The methods are described in Hothorn et al. (2006) <doi:10.1198/106186006X133933>, Zeileis et al. (2008) <doi:10.1198/106186008X319331> and Strobl et al. (2007) <doi:10.1186/1471-2105-8-25>.
PackageMaintainerDebian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageNamer-cran-party
PackageSectiongnu-r
PackageVersion1.3-9-1
SHA-1F28244FAA11164F3C9BB97BCA25F19B8E1BCDC3C
SHA-25607446BF69E81BE72D4DA138837C3298BE9B2AF3B7379B3814AC373C44012E945