Last data update: 2014.03.03

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CranContrib
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Results 1 - 4 of 4 found.
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PSAboot : Bootstrapping for Propensity Score Analysis

Package: PSAboot
Type: Package
Title: Bootstrapping for Propensity Score Analysis
Version: 1.1.4
Date: 2016-02-25
Author: Jason Bryer <jason@bryer.org>
Maintainer: Jason Bryer <jason@bryer.org>
URL: http://jason.bryer.org/PSAboot
BugReports: https://github.com/jbryer/PSAboot/issues
Description: Bootstrapping for propensity score analysis and matching.
License: GPL
LazyLoad: yes
VignetteBuilder: knitr
Depends: R (>= 3.0), graphics, ggplot2, PSAgraphics
Imports:
utils,Matching,MatchIt,modeltools,parallel,party,psych,reshape2,rpart,TriMatch,ggthemes,stats
Suggests: devtools, knitr, multilevelPSA
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-02-26 01:10:24 UTC; jbryer
Repository: CRAN
Date/Publication: 2016-02-26 05:49:19

● Data Source: CranContrib
● 0 images, 26 functions, 1 datasets
● Reverse Depends: 0

flexclust : Flexible Cluster Algorithms

Package: flexclust
Version: 1.3-4
Date: 2013-07-02
Authors@R: c(person(given="Friedrich", family="Leisch",
email="Friedrich.Leisch@R-project.org", role=c("aut", "cre")),
person(given="Evgenia", family="Dimitriadou", role="ctb"))
Title: Flexible Cluster Algorithms
Depends: R (>= 2.14.0), graphics, grid, lattice, modeltools
Imports: methods, parallel, stats, stats4
Suggests: ellipse, clue, cluster, seriation
Description: The main function kcca implements a general framework for
k-centroids cluster analysis supporting arbitrary distance
measures and centroid computation. Further cluster methods
include hard competitive learning, neural gas, and QT
clustering. There are numerous visualization methods for
cluster results (neighborhood graphs, convex cluster hulls,
barcharts of centroids, ...), and bootstrap methods for the
analysis of cluster stability.
License: GPL-2
LazyLoad: yes
Packaged: 2013-07-02 10:30:20 UTC; leisch
Author: Friedrich Leisch [aut, cre], Evgenia Dimitriadou [ctb]
Maintainer: Friedrich Leisch <Friedrich.Leisch@R-project.org>
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2013-07-02 12:32:50

● Data Source: CranContrib
● 0 images, 28 functions, 9 datasets
Reverse Depends: 3

archetypes : Archetypal Analysis

Package: archetypes
Type: Package
Title: Archetypal Analysis
Version: 2.2-0
Date: 2014-04-08
Depends: methods, stats, modeltools, nnls (>= 1.1)
Suggests: MASS, vcd, mlbench, ggplot2, TSP
Authors@R: c(person("Manuel", "J. A. Eugster", role = c("aut", "cre"), email =
"manuel@mjae.net"), person("Friedrich", "Leisch", role = "aut"),
person("Sohan", "Seth", role = "ctb"))
Description: The main function archetypes implements a
framework for archetypal analysis supporting arbitrary
problem solving mechanisms for the different conceptual
parts of the algorithm.
License: GPL (>= 2)
Collate: 'archetypes-barplot.R' 'generics.R' 'archetypes-class.R'
'archetypes-kit-blocks.R' 'archetypes-kit.R' 'archetypes-map.R'
'archetypes-movie.R' 'archetypes-panorama.R' 'pcplot.R'
'archetypes-pcplot.R' 'archetypes-robust.R'
'archetypes-screeplot.R' 'archetypes-step.R'
'archetypes-weighted.R' 'archetypes-xyplot.R' 'memento.R'
'simplex-pot.R' 'skeletonplot.R'
Packaged: 2014-04-10 06:03:03 UTC; eugstem1
Author: Manuel J. A. Eugster [aut, cre],
Friedrich Leisch [aut],
Sohan Seth [ctb]
Maintainer: Manuel J. A. Eugster <manuel@mjae.net>
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-04-10 09:17:57

● Data Source: CranContrib
● 0 images, 35 functions, 3 datasets
● Reverse Depends: 0

party : A Laboratory for Recursive Partytioning

Package: party
Title: A Laboratory for Recursive Partytioning
Date: 2015-11-04
Version: 1.0-25
Authors@R: c(person("Torsten", "Hothorn", role = c("aut", "cre"),
email = "Torsten.Hothorn@R-project.org"),
person("Kurt", "Hornik", role = "aut"),
person("Carolin", "Strobl", role = "aut"),
person("Achim", "Zeileis", role = "aut"))
Description: 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.
Depends: R (>= 2.14.0), methods, grid, stats, mvtnorm (>= 1.0-2),
modeltools (>= 0.2-21), strucchange
LinkingTo: mvtnorm
Imports: survival (>= 2.37-7), coin (>= 1.1-0), zoo, sandwich (>=
1.1-1)
Suggests: TH.data (>= 1.0-3), mlbench, colorspace, MASS, vcd, ipred
LazyData: yes
License: GPL-2
URL: http://party.R-forge.R-project.org
NeedsCompilation: yes
Packaged: 2015-11-05 18:26:29 UTC; hothorn
Author: Torsten Hothorn [aut, cre],
Kurt Hornik [aut],
Carolin Strobl [aut],
Achim Zeileis [aut]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>
Repository: CRAN
Date/Publication: 2015-11-05 20:04:04

● Data Source: CranContrib
● Cran Task View: Environmetrics, MachineLearning, Multivariate, Survival
● 0 images, 24 functions, 1 datasets
Reverse Depends: 4