Result for 105F128D84FDE251BFA8C685C68C7549ACCE5C9D

Query result

Key Value
FileName./usr/lib/R/site-library/party/R/party.rdb
FileSize401201
MD5A8EDC182D02323C912D6022EB17C2526
SHA-1105F128D84FDE251BFA8C685C68C7549ACCE5C9D
SHA-256B4CED5CC3EFBF6644BF87FE2F73A2DA7B46F574AEA3FD8583B99D1A4B72E5A94
SSDEEP6144:i22RYEjpd+hhQW6HQW6AppGp9zBcgn7WbqeerUkqONfB5ajKHb7iDZxtOlMw:ixLjH+j+H+uUiLb1QeCYG7iDftaMw
TLSHT1B984239D9F1D614D7A404C686027D1B0A12E24D43C54E8DE6B02AF0FF6FE8A4F9C96F9
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
FileSize1131144
MD53175E79514C0719E63BB488438B56A3E
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-185272FB41B3E31B3D95541C482EBD0E60E52E4E0
SHA-25682BD0D347228F76BCCB5C241D385BF85C82A3CFA398B045357F654EE852DFA3F