Parents (Total: 17)

The searched file hash is included in 17 parent files which include package known and seen by metalookup. A sample is included below:

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	4BD28BD3BC92E16935B649677410CE56
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. 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>.
PackageName	R-party
PackageRelease	1.7
PackageVersion	1.3.9
SHA-1	1E223784871ADC168F2A715B90D3DE0038F80038
SHA-256	1207687EEDB01594E798F2878560ECDF8741D99DCD1976967422EB8D3EC01E8F

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	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	0A7E6BB4C7AA1FB5001D84A763675717
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. 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>.
PackageName	R-party
PackageRelease	lp153.1.2
PackageVersion	1.3.9
SHA-1	38DAA0745CEBB5CDF16D67DFEBE310B8E9915D8E
SHA-256	35DC8C0DA315052D1E246E39C58FE31638C283486BCBA1F88F6839E3D65080AA

Key	Value
MD5	D1D6722A21769A3E80A2B23A5E449E86
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. 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>.
PackageName	R-party
PackageRelease	lp154.1.1
PackageVersion	1.3.9
SHA-1	43AD04630C57656B018C8ECE7CA23579B02A32FD
SHA-256	A925B76A44B0FDC2D6812F6A7049C3DFB09CF0983F6CD85C4823F6A9AC3BFA77

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	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

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	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