Parents (Total: 15)

The searched file hash is included in 15 parent files which include package known and seen by metalookup. A sample is included below:

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

Key	Value
MD5	79524F0ED28AC4F46FCB33577A86FFA4
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	lp153.2.12
PackageVersion	1.0_17
SHA-1	24BB30BE1CE7977CCB97AD6DF0BA112CB0DD13B2
SHA-256	F44B41AD247FCB385E85460B9ECBA743F2107D685AF2977922C5D1B4D759AB0D

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	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	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	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	EAC63060FAFDF642E92E215EAFDD0E0A
PackageArch	armv7hl
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	2.137
PackageVersion	1.0_17
SHA-1	9D5B2E27FE7D57FF0E2E7C941415EB20834958F8
SHA-256	787945C6329A01E8CA43E8C604FB7EB0C38B085A63D408F4F11D26772EF04973

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

Key	Value
MD5	5E8AE91AC500978B9008AE313FD63B2E
PackageArch	i586
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	14E853F11B100F7C4ABFCEB24901C3E6A9451E2A
SHA-256	F29A270B3BB402E0EDA197C2A8E6FA4A13987B43A0B1637B2D5627E6CE32E0BB

Key	Value
MD5	86DD76524893FBB2919BEB4651905A5A
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	2.16
PackageVersion	1.0_17
SHA-1	48D273EEC8CE7E3F37639450B36278C3EBD61798
SHA-256	22547B8AACA9E920B5D007B0E41E3E6ABD8449FE0EC7AB1E973C86A06012A6DE

Key	Value
MD5	826CB6B6E028CFA84AB65B2DF49D1240
PackageArch	i586
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	lp150.2.4
PackageVersion	1.0_17
SHA-1	062E201217A25CD976DC7A80738E63F8605F0CBC
SHA-256	D126A57C473924293B26DECDE5F755432A8F2CC1CF6B8120BFA36B3A522E4F28

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

Key	Value
MD5	511BEE050AB20DF591E06AF38B3F4978
PackageArch	i586
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	5216A9CE65D2D5F047DA850A42A8F3266F69247C
SHA-256	1C1E6F418AE21EC3B0E1AD072288004940607B9C8695FAC6B49F91C3AD01AEE9

Key	Value
MD5	80CD3454E2DE002152D5665B87D23DE9
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	2.6
PackageVersion	1.0_17
SHA-1	AF2DE7DAA38B0D075E5C440EDC951FEDFB6309F9
SHA-256	72EC25474942C0D7D11015F7A2160439E48E9CD0E487B1166E0C5E01F734CD46

Key	Value
MD5	FF27E6B1ECF590EAC437FF28AB252376
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	lp151.2.58
PackageVersion	1.0_17
SHA-1	511475C7247778897F6B53926C43759DF29C7243
SHA-256	7D7F3714F458DDD5C6CB083F2445B030D5BB89A969D6B512D0A1800D11D31EE2