Parents (Total: 21)

The searched file hash is included in 21 parent files which include package known and seen by metalookup. A sample is included below:

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
FileSize	1126716
MD5	2EB41D1A84C9F14E0F2ED68FCDB0E257
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	4AA858BE406B3CDE36477ED47D987860E2259171
SHA-256	226FAAF6F2A52DF0BF5C3938247FEBD21EA97E9BA3A843901ABA1CCB48ABF9F5

Key	Value
FileSize	1158640
MD5	D83D0EF05AB8F3441E884BE50FBA378E
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	1.3-5-1
SHA-1	551013D15CE99326D2D6D3F3F17C66E3CF497C90
SHA-256	A9ED2D82C25F9DCD164C0EF46E866791B78A45131061DC454DC8646E33F6EC13

Key	Value
FileSize	1133224
MD5	3183036710F01F60E546F36855302F52
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	5B65F7212B83829E135737D5582AB1E3295E3D76
SHA-256	DCCF2CA6F71C546A95694CD2E6B056A7ED0B47D2F13BDBD9F31DE5DFEF8CEC6D

Key	Value
FileSize	1158172
MD5	C6EFFD0765B632463F1A929B27A2D673
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	1.3-5-1
SHA-1	5F4B65716718214E3CE338B7CED666917D967C0A
SHA-256	F63C33758568DA071EA0F42F69030C5EB50A070B5D1D9A3BF1032B90C197AE4C

Key	Value
FileSize	1161468
MD5	C5C437E36A67C0D583A93205C7752FF8
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	1.3-5-1
SHA-1	6030E1381526D75C400389EF0B631F9A0C886EC7
SHA-256	F94F6734C13716C12132B1ED9F1102F76976E2CF9CCE01487DAB5749EF6DFF5D

Key	Value
FileSize	1164388
MD5	310E3A70002AA3EB3566670F770A22EA
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	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	r-cran-party
PackageSection	gnu-r
PackageVersion	1.3-5-1
SHA-1	67B67433AE3D36BFB873B3BD119298B0945C026A
SHA-256	BF55ADFE45E22B33C69BD6BDA043CA654DEDDDE852CF848E6882ABDFCC2E8182

Key	Value
FileSize	1167592
MD5	A8D95D9353B21EB38A21DC71C7C79F29
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	1.3-5-1
SHA-1	7F07D5B576927B863BE0E203E65D24050A5E913F
SHA-256	07CF55C9CC2ABD7B349534D5280727CB640B4D3EB2C802DA368F3DAA989A5D54