Result for 09C2919C64DCE0FA586568A5665F6ED2A4CFD12F

Query result

Key Value
FileName./usr/lib/R/site-library/party/R/party.rdb
FileSize401233
MD5DB46B45B6C4FFF28032FDC3286A582C9
SHA-109C2919C64DCE0FA586568A5665F6ED2A4CFD12F
SHA-25622BA8DEA55B58E7AC6E6FDDE8BC66726C946095C8C216B0CCD0585441754A002
SSDEEP6144:i22RYEjpddwZ0z1UppGp9zBcgn7WbqeerUkqONfB5ajKHb7iDZxtOlMw:ixLjHd2XUiLb1QeCYG7iDftaMw
TLSHT12B84235DBF5C618EB6400D64511AD1B0941C30A17844E8EE6712EB4FB6FACB4F9CC6BE
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
FileSize1133224
MD53183036710F01F60E546F36855302F52
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-15B65F7212B83829E135737D5582AB1E3295E3D76
SHA-256DCCF2CA6F71C546A95694CD2E6B056A7ED0B47D2F13BDBD9F31DE5DFEF8CEC6D