Result for 193339C4379AA429274D6C4357C78A4FC31D19A5

Query result

Key Value
FileName./usr/lib/R/site-library/party/libs/party.so
FileSize80220
MD5C545FEB4C19702552459894BEB675998
SHA-1193339C4379AA429274D6C4357C78A4FC31D19A5
SHA-256A321F990D66485B48BF2BE04422F4FC4247DEB2E831BBB962D5CDFD39D1E96EC
SSDEEP1536:k8uSK+Cyg0/fNBf+KWnDN9QUa4SuL9iqXkRObO+pjJD7yH:HnlfgQE9QsSuL9i4CWXpj
TLSHT11A73EB8AFCC14E6AC6D4133A739D8764612313B0D3D93B13D42D9B393ADA6AF0519F92
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
FileSize1123612
MD539B7DD9BAED97CF9DF4C69523F72D8D8
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-1966F4D591EA289090D9D48175D2C989051B4686E
SHA-256930CF21D27694A2D1F8EB9472ABECE3231C8971A5CD98323885B453825BBFBD9