Result for 07D2B9AC37760A77F833F8FEE69E251C4C32EE5E

Query result

Key Value
FileName./usr/lib/R/site-library/party/R/party.rdb
FileSize399670
MD576DCFC4EBA6AA75A7177F617871E1A79
SHA-107D2B9AC37760A77F833F8FEE69E251C4C32EE5E
SHA-256FB46587418B0A75DF620FF618E03C0C0524F592B629BD4EF57423B1842BD8EBC
SSDEEP12288:uAAOo+l+kzmbBTE/UiqvcP9RKx+4g7EA2ZISWZ:uAAO10k+5hvcP9dxQIBZ
TLSHT1C184239BC7F2524801CD3FA4E50588DE9C9B6822E9586E614041F6AD18ECE8D7786FFC
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