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	AA6977FF5C89F978FB45E35A459E5EEE
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	lp152.2.19
PackageVersion	1.0_17
SHA-1	FDF9A5FDC0A3607D48AA6C23D872C44DA46E4DDE
SHA-256	4F43D18C24189E10F3F3267AFE35A96EA3DC1E52A8B51CC4A8550224BA6E8628

Key	Value
MD5	7739C3E1F464078730251EBD84F826AD
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	lp152.2.14
PackageVersion	1.0_17
SHA-1	7F191CA0E47F4E6BB2351F64F025C844C2387E88
SHA-256	4353AD00F9A5AF72B9A0839BD4C9469B970AE07EC3EBE19E5E08C079C6BC6C81