Result for 199076FE9E6436C0780AC532194D7FD3AC501D88

Query result

Key Value
FileName./usr/lib/R/site-library/party/R/party.rdx
FileSize2795
MD5D400FC79444CF60C4C7BEE907246035D
SHA-1199076FE9E6436C0780AC532194D7FD3AC501D88
SHA-256B1CC1B5ABFBC778C9D72D0BBD7F9B6DCD0CA22053F04B39B6AEB3BD76506A830
SSDEEP48:XFIsxUuw90hJOFNXGGgwV3wu+zKYn3QXFFfpM3F7mLatuBTDHPT:isauwChJy3wDni147mNnL
TLSHT18E514B0AA8ACF6827C464367E7B35F800093B4FA5AB9865F000C766E3E4721611E5BBC
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
FileSize1121744
MD58F3ED2CACAA9D148C17A011465D73C07
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-1F28244FAA11164F3C9BB97BCA25F19B8E1BCDC3C
SHA-25607446BF69E81BE72D4DA138837C3298BE9B2AF3B7379B3814AC373C44012E945