| Key | Value |
|---|---|
| FileName | ./usr/lib/R/site-library/party/CITATION |
| FileSize | 3957 |
| MD5 | D939B5D2A70FE896889921A02588E794 |
| SHA-1 | 2AA820C995EC204E64694817451D4255EC5FE0AB |
| SHA-256 | 9BE372EA5FF731AC1967C191691A3238B2AE08602BA1C63110B068B247908A67 |
| SSDEEP | 96:aMy+P7Cmr1+DT1Xp+3ynPptqSYiz2jlV5q6TO2OX:N++Cfyi61NtOX |
| TLSH | T1A981BA06EA431A3139C6A24C9D9680011BB7503FAC00B9747A5D469C2F2F5AFA1FBF3D |
| hashlookup:parent-total | 27 |
| hashlookup:trust | 100 |
The searched file hash is included in 27 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 | 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 | 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 |
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| Key | Value |
|---|---|
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| 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 |
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| Key | Value |
|---|---|
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| 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 |
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| Key | Value |
|---|---|
| FileSize | 1158640 |
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| 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 |
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| SHA-1 | 551013D15CE99326D2D6D3F3F17C66E3CF497C90 |
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| FileSize | 1158172 |
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| 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 |
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| FileSize | 1161468 |
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| 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 |
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