Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	807DAAA7F41802D653CEFEA595AC6832
PackageArch	x86_64
PackageDescription	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.
PackageName	R-party
PackageRelease	2.27
PackageVersion	1.0_17
SHA-1	386341E1940CE74D89717A723EB4E81FF41C0265
SHA-256	E7FAC464946CBC8D35577A74052BC7485B64A668ED8EFC86B6025D03A8607795

Key	Value
MD5	C27842E913B9A9458199CD040B2F588F
PackageArch	x86_64
PackageDescription	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.
PackageName	R-party
PackageRelease	lp150.2.49
PackageVersion	1.0_17
SHA-1	A55B0292973E0C14F641D340FCC254B86E92C77F
SHA-256	6F1F69107E99CD53A5F243C5BC979CA791AD62D1931B31BE123E559302FCDDBF