Result for 1D632AF9B472C3F39EF0A84CA30E9D171A8D0DE4

Query result

Key Value
FileName./usr/lib/R/site-library/party/Meta/package.rds
FileSize1820
MD58F3EA1F947F4252D7FE4EEF938D1248C
SHA-11D632AF9B472C3F39EF0A84CA30E9D171A8D0DE4
SHA-2567B6FFFD10FB7E928240CBBB1CA8100508767AAA865165A8D3286EC66A01041D3
SSDEEP24:XFgCU0698Dq2aGPidN5ITjpK0P1r9CHCO7GuHjLEVZcfuBKhuhtFKhVgrpek5NJr:X/UTu81NuX9rgJfkrdy+Kcrpek5NF
TLSHT1AA314BE2A60E0FCA336921338DEFA3F5831500BB50422A09B849E01D283598C2423E69
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
FileSize1161468
MD5C5C437E36A67C0D583A93205C7752FF8
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-16030E1381526D75C400389EF0B631F9A0C886EC7
SHA-256F94F6734C13716C12132B1ED9F1102F76976E2CF9CCE01487DAB5749EF6DFF5D