Package: InvariantCausalPrediction
Type: Package
Title: Invariant Causal Prediction
Version: 0.6-1
Date: 2016-05-02
Author: Nicolai Meinshausen
Depends: glmnet, mboost
Maintainer: Nicolai Meinshausen <meinshausen@stat.math.ethz.ch>
Description: Confidence intervals for causal effects, using data collected in different experimental or environmental conditions. Hidden variables can be included in the model with a more experimental version.
License: GPL
NeedsCompilation: no
Packaged: 2016-05-02 11:31:42 UTC; nicolai
Repository: CRAN
Date/Publication: 2016-05-02 14:04:03
Package: CAM
Type: Package
Title: Causal Additive Model (CAM)
Version: 1.0
Date: 2015-03-03
Author: Jonas Peters and Jan Ernest
Encoding: UTF-8
Depends: glmnet, mboost, Matrix, parallel, mgcv
Maintainer: Jonas Peters <jonas.peters@tuebingen.mpg.de>
Description: The code takes an n x p data matrix and fits a Causal Additive Model (CAM) for estimating the causal structure of the underlying process. The output is a p x p adjacency matrix (a one in entry (i,j) indicates an edge from i to j). Details of the algorithm can be found in: P. Bühlmann, J. Peters, J. Ernest: "CAM: Causal Additive Models, high-dimensional order search and penalized regression", Annals of Statistics 42:2526-2556, 2014.
License: FreeBSD
Packaged: 2015-03-05 17:14:16 UTC; jopeters
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-03-05 22:01:05
Package: globalboosttest
Version: 1.1-0
Date: 2010-08-02
Title: Testing the additional predictive value of high-dimensional data
Author: Anne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>,
Torsten Hothorn <torsten.hothorn@stat.uni-muenchen.de>.
Maintainer: Anne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>
Depends: R (>= 2.8), mboost (>= 2.0-0), survival
Suggests:
Description: 'globalboosttest' implements a permutation-based testing
procedure to globally test the (additional) predictive value of
a large set of predictors given that a small set of predictors
is already available. Currently, 'globalboosttest' supports
binary outcomes (via logistic regression) and survival outcomes
(via Cox regression). It is based on boosting regression as
implemented in the package 'mboost'.
License: GPL (>= 2)
URL:
Repository: CRAN
Repository/R-Forge/Project: globalboosttest
Repository/R-Forge/Revision: 4
Date/Publication: 2012-10-29 08:58:54
Packaged: 2012-10-29 08:58:54 UTC; ripley
Package: parboost
Title: Distributed Model-Based Boosting
Version: 0.1.4
Date: 2015-05-03
Description: Distributed gradient boosting based on the mboost package. The
parboost package is designed to scale up component-wise functional
gradient boosting in a distributed memory environment by splitting the
observations into disjoint subsets, or alternatively using bootstrap
samples (bagging). Each cluster node then fits a boosting model to its
subset of the data. These boosting models are combined in an ensemble,
either with equal weights, or by fitting a (penalized) regression
model on the predictions of the individual models on the complete
data.
Author: Ronert Obst <ronert.obst@gmail.com>
Maintainer: Ronert Obst <ronert.obst@gmail.com>
Depends: R (>= 3.0.1), parallel, mboost, party, iterators
Imports: plyr, caret, glmnet, doParallel
License: GPL-2
NeedsCompilation: no
Packaged: 2015-05-03 16:27:09 UTC; ronert
Repository: CRAN
Date/Publication: 2015-05-04 01:24:31
providing 
.