Parents (Total: 3)

The searched file hash is included in 3 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	F3B20F35BAC641ED70F3BC17DFB6E47C
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	2.237
PackageVersion	1.0_17
SHA-1	8D29A600D559F39A49424E3059FEA5963C950A50
SHA-256	DA7E9F7635D903C76EF2BEA790516263D0A0F3B69CC70BF56B3CD168D2CB4C35

Key	Value
MD5	BB61550FF077624CB9F7F869C6961D84
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	2.15
PackageVersion	1.0_17
SHA-1	A68BCBC58539B89C3A925332FC6F17F044D70375
SHA-256	D2B0E2C6812A4AC0D2F42FBFE385BA10695ABAE485C828E1EC992DEF3EE72CAA

Key	Value
MD5	8EACAC70619C915321BFEC40EFF06F6A
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	2.237
PackageVersion	1.0_17
SHA-1	E2C5ACA09ABD6BDCB1820A15126B990762F87142
SHA-256	DB9D59BFC341B057C7FE301AF841E8B1FEEAD076E0E1C2283F459AC439F8EE38