Package: multiPIM
Version: 1.4-3
Date: 2015-02-24
Title: Variable Importance Analysis with Population Intervention Models
Author: Stephan Ritter <sritter@berkeley.edu>, Alan Hubbard <hubbard@berkeley.edu>, Nicholas Jewell <jewell@berkeley.edu>
Maintainer: Stephan Ritter <stephanritterRpacks@gmail.com>
Depends: lars (>= 0.9-8), penalized, polspline, rpart
Suggests: parallel
LazyLoad: yes
Description: Performs variable importance analysis using a causal inference approach. This is done by fitting Population Intervention Models. The default is to use a Targeted Maximum Likelihood Estimator (TMLE). The other available estimators are Inverse Probability of Censoring Weighted (IPCW), Double-Robust IPCW (DR-IPCW), and Graphical Computation (G-COMP) estimators. Inference can be obtained from the influence curve (plug-in) or by bootstrapping.
License: GPL (>= 2)
URL: http://www.jstatsoft.org/v57/i08/
Packaged: 2015-02-25 04:52:31 UTC; sritter
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-02-25 08:12:42
Package: maboost
Version: 1.0-0
Date: 2014-11-01
Title: Binary and Multiclass Boosting Algorithms
Author: Tofigh Naghibi
Depends: R (>= 2.10), rpart, C50
Description: Performs binary and multiclass boosting in maximum-margin, sparse, smooth and normal settings
as described in "A Boosting Framework on Grounds of Online Learning" by T. Naghibi and B. Pfister, (2014).
For further information regarding the algorithms, please refer to http://arxiv.org/abs/1409.7202
Maintainer: Tofigh Naghibi <tofigh@gmail.com>
License: GPL (>= 2)
Packaged: 2014-11-25 15:05:38 UTC; tofighn
Repository: CRAN
Date/Publication: 2014-11-25 17:03:41
Collate: 'maboost-package.r' 'maboost.R' 'maboost.default.R'
'maboost.formula.R' 'maboost.machine.bin.R'
'maboost.machine.multi.R' 'predict.maboost.R' 'print.maboost.R'
'projsplx.R' 'projsplx_k.R' 'summary.maboost.R'
'update.maboost.R' 'varplot.maboost.R'
NeedsCompilation: no
Package: maptree
Version: 1.4-7
Date: 2012-11-24
Title: Mapping, pruning, and graphing tree models
Author: Denis White, Robert B. Gramacy <rbgramacy@chicagobooth.edu>
Maintainer: Robert B. Gramacy <rbgramacy@chicagobooth.edu>
Depends: R (>= 2.14), cluster, rpart
Description: Functions with example data for graphing, pruning, and
mapping models from hierarchical clustering, and classification
and regression trees.
License: Unlimited
Packaged: 2012-11-24 18:43:40 UTC; bobby
Repository: CRAN
Date/Publication: 2012-11-26 07:28:04
Package: DStree
Type: Package
Title: Recursive Partitioning for Discrete-Time Survival Trees
Version: 1.0
Date: 2014-08-19
Author: Peter Mayer, Denis Larocque, Matthias Schmid
Maintainer: Peter Mayer <mayerptr@gmail.com>
Description: Building discrete-time survival trees and bagged trees based on
the functionalities of the rpart package. Splitting criterion maximizes the
likelihood of a covariate-free logistic discrete time hazard model.
License: GPL-2
NeedsCompilation: no
Depends: rpart, pec, Ecdat
Imports: rpart.plot, survival, Rcpp
LazyData: no
LinkingTo: Rcpp
Packaged: 2014-08-19 18:15:30 UTC; Peter
Repository: CRAN
Date/Publication: 2014-08-19 22:25:27
Package: DidacticBoost
Type: Package
Title: A Simple Implementation and Demonstration of Gradient Boosting
Version: 0.1.1
Date: 2016-04-19
Authors@R: person("David", "Shaub", email = "davidshaub@gmx.com", role = c("aut", "cre"))
Description: A basic, clear implementation of tree-based gradient boosting
designed to illustrate the core operation of boosting models. Tuning
parameters (such as stochastic subsampling, modified learning rate, or
regularization) are not implemented. The only adjustable parameter is the
number of training rounds. If you are looking for a high performance boosting
implementation with tuning parameters, consider the 'xgboost' package.
License: GPL-3
Depends: R (>= 3.1.1), rpart (>= 4.1-10)
Suggests: testthat
URL: https://github.com/dashaub/DidacticBoost
BugReports: https://github.com/dashaub/DidacticBoost/issues
ByteCompile: true
NeedsCompilation: no
LazyData: TRUE
RoxygenNote: 5.0.1
Packaged: 2016-04-19 01:46:08 UTC; david
Author: David Shaub [aut, cre]
Maintainer: David Shaub <davidshaub@gmx.com>
Repository: CRAN
Date/Publication: 2016-04-19 08:11:59
Package: NHEMOtree
Type: Package
Title: Non-hierarchical evolutionary multi-objective tree learner to
perform cost-sensitive classification
Depends: partykit, emoa, sets, rpart
Version: 1.0
Date: 2013-05-05
Author: Swaantje Casjens
Maintainer: Swaantje Casjens <swaantje.casjens@tu-dortmund.de>
Description: NHEMOtree performs cost-sensitive classification by
solving the two-objective optimization problem of minimizing
misclassification rate and minimizing total costs for
classification. The three methods comprised in NHEMOtree are
based on EMOAs with either tree representation or bitstring
representation with an enclosed classification tree algorithm.
License: GPL-3
Packaged: 2013-05-09 09:55:32 UTC; hendrik
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-05-09 15:46:02
Package: PSAgraphics
Version: 2.1.1
Date: 2012-03-18
Title: Propensity Score Analysis Graphics
Author: James E. Helmreich <James.Helmreich@Marist.edu> and Robert M.
Pruzek <RMPruzek@yahoo.com>. We are grateful to KuangNan Xiong
for significant work on the functions new to version 2.0:
cstrata.psa, cv.bal.psa, and cv.trans.psa.
Maintainer: James E. Helmreich <James.Helmreich@Marist.edu>
Depends: R (>= 2.14.0), rpart
Description: A collection of functions that primarily produce graphics
to aid in a Propensity Score Analysis (PSA). Functions
include: cat.psa and box.psa to test balance within strata of
categorical and quantitative covariates, circ.psa for a
representation of the estimated effect size by stratum,
loess.psa that provides a graphic and loess based effect size
estimate, and various balance functions that provide measures
of the balance achieved via a PSA in a categorical covariate.
License: GPL (>= 2)
Packaged: 2012-03-18 18:07:20 UTC; ilfautetre
Repository: CRAN
Date/Publication: 2012-03-18 18:39:58
Package: REEMtree
Type: Package
Title: Regression Trees with Random Effects for Longitudinal (Panel)
Data
Version: 0.90.3
Date: 2011-07-15
Author: Rebecca Sela and Jeffrey Simonoff
Maintainer: Rebecca Sela <rsela@stern.nyu.edu>
Depends: nlme, rpart, methods
Suggests: AER
Description: This package estimates regression trees with random
effects as a way to use data mining techniques to describe
longitudinal or panel data.
License: GPL
URL: http://pages.stern.nyu.edu/~jsimonof/REEMtree/
Packaged: Sun Aug 7 20:38:07 2011; rsela
Repository: CRAN
Date/Publication: 2011-08-08 05:41:32
Package: GPLTR
Type: Package
Title: Generalized Partially Linear Tree-Based Regression Model
Version: 1.2
Date: 2015-06-16
Author: Cyprien Mbogning <cyprien.mbogning@inserm.fr> and Wilson Toussile
Maintainer: Cyprien Mbogning <cyprien.mbogning@gmail.com>
Description: Combining a generalized linear model with an additional tree part
on the same scale. A four-step procedure is proposed to fit the model and test
the joint effect of the selected tree part while adjusting on confounding factors.
We also proposed an ensemble procedure based on the bagging to improve prediction
accuracy and computed several scores of importance for variable selection.
License: GPL (>= 2.0)
LazyLoad: yes
Depends: rpart , parallel
Packaged: 2015-06-18 14:09:08 UTC; elodie
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-06-18 18:00:14
providing 
.