Result for 13FD2F8D5E19E2A97E0D91B844F304C312DCE92C

Query result

Key Value
FileName./usr/lib/R/site-library/party/help/paths.rds
FileSize384
MD52B39C14758BFD0A39CADB5C07E98AD15
SHA-113FD2F8D5E19E2A97E0D91B844F304C312DCE92C
SHA-2569E33ED345668D5DE53A982C7D0BF0C1046041409BA1828A75CB827A6B15BB397
SSDEEP6:XtV9aPg+hVf1IluTH8tZDZWEmOoUkcMEZRqLtWin//30I:XdEVSKHsDbmJRcMoR+tWinXEI
TLSHT1F8E0F1B2778D59217FBFC4B51470608191C782484AA2049DCA734E09A42222111FCD19
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
FileSize1123612
MD539B7DD9BAED97CF9DF4C69523F72D8D8
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-1966F4D591EA289090D9D48175D2C989051B4686E
SHA-256930CF21D27694A2D1F8EB9472ABECE3231C8971A5CD98323885B453825BBFBD9