Result for 1815CA5096FA4A9388E6AEA282367E60DA91A292

Query result

Key Value
FileName./usr/lib/R/site-library/party/R/party.rdb
FileSize401235
MD562346F654C8DFA7D37B6C987CC382F2A
SHA-11815CA5096FA4A9388E6AEA282367E60DA91A292
SHA-25643F85E50F760DE2DFAC50329AC3A224D99A2826E562FEB7487752D0469F13F2A
SSDEEP6144:i22RYEjpdoPmz1UppGp9zBcgn7WbqeerUkqONfB5ajKHb7iDZxtOlMw:ixLjHoxUiLb1QeCYG7iDftaMw
TLSHT17F84135DBF5C618EB6400C64521AD1B0651C30917844E8EF6712AF1BB7FBCA4FACC6BA
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
FileSize1122124
MD5568E2077250208E7675FDE0D04706FBF
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-14139BFBA3AD16717DEEB8575C0AD37487B913570
SHA-256616D18F3D416A52727631264A9C993A9ED95B34C413D5BCC670A4F08CAE27A22