Parents (Total: 40)

The searched file hash is included in 40 parent files which include package known and seen by metalookup. A sample is included below:

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	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
FileSize	1125056
MD5	F5EF1E391DE443A3CAD4D5FABC05C80F
PackageDescription	GNU R laboratory for recursive partytioning 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>.
PackageMaintainer	Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageName	r-cran-party
PackageSection	gnu-r
PackageVersion	1.3-9-1
SHA-1	3BC563215E582514241FB890BFC487C4577494B0
SHA-256	129054A8E5F81BAEB4D75F69D22598CA802BB1DD63974CB948E844FC53E38419

Key	Value
FileSize	1121624
MD5	A7E26CDE5A7C0AC5F9B89A52500ED722
PackageDescription	GNU R laboratory for recursive partytioning 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>.
PackageMaintainer	Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageName	r-cran-party
PackageSection	gnu-r
PackageVersion	1.3-9-1
SHA-1	3C8423F464F92A7F528F100312D9B888D34BEB12
SHA-256	2C74C31BE18ECB0AE6830422AAD22E1DB0B9D7E25E1DEA88F829CFB1A99155FD

Key	Value
FileSize	1122124
MD5	568E2077250208E7675FDE0D04706FBF
PackageDescription	GNU R laboratory for recursive partytioning 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>.
PackageMaintainer	Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageName	r-cran-party
PackageSection	gnu-r
PackageVersion	1.3-9-1
SHA-1	4139BFBA3AD16717DEEB8575C0AD37487B913570
SHA-256	616D18F3D416A52727631264A9C993A9ED95B34C413D5BCC670A4F08CAE27A22

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