Result for 158B6CD5A6E2B26FBBF3F475EEF916BF0BC20214

Query result

Key Value
FileName./usr/lib/R/site-library/party/DESCRIPTION
FileSize2513
MD523866D3EEC1FA14D2A8C1CAC51701DE8
SHA-1158B6CD5A6E2B26FBBF3F475EEF916BF0BC20214
SHA-25611E7CDE3C5B59A98DD2E7CE7FA9FF8FC4C90F17276CB3EC8F42F26FC4435D865
SSDEEP48:epNi08rwt5YvjUbp99yBpGouP9UQJr95NxT1B1Ll4Xnmxo6jm/9Wb:e3i0ywjYbYvM8P9TJrfX1Z4Xmt29Wb
TLSHT11951A3017C21A582778BE2182672A605B3AF61597DB6386C716C04B81B2E95C4AFB70C
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
FileSize1158172
MD5C6EFFD0765B632463F1A929B27A2D673
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
PackageVersion1.3-5-1
SHA-15F4B65716718214E3CE338B7CED666917D967C0A
SHA-256F63C33758568DA071EA0F42F69030C5EB50A070B5D1D9A3BF1032B90C197AE4C