Package: mht
Type: Package
Title: Multiple Hypothesis Testing for Variable Selection in
High-Dimensional Linear Models
Version: 3.1.2
Author: Florian Rohart
Maintainer: Florian Rohart <florian.rohart@gmail.com>
Description: Multiple Hypothesis Testing For Variable Selection in high dimensional linear models. This package performs variable selection with multiple hypothesis testing, either for ordered variable selection or non-ordered variable selection. In both cases, a sequential procedure is performed. It starts to test the null hypothesis "no variable is relevant"; if this hypothesis is rejected, it then tests "only the first variable is relevant", and so on until the null hypothesis is accepted.
License: GPL-3
Depends: glmnet, Matrix
Packaged: 2015-03-21 23:15:11 UTC; florian
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2015-03-23 07:33:03
Package: lassoscore
Title: High-Dimensional Inference with the Penalized Score Test
Description: Use the lasso regression method to perform approximate inference
in high dimensions, by penalizing the effects of nuisance parameters.
Version: 0.6
Author: Arie Voorman <arie.voorman@gmail.com>
Maintainer: Arie Voorman <arie.voorman@gmail.com>
Depends: R (>= 2.10), glasso, glmnet, Matrix
Suggests: covTest, lars
License: GPL (>= 2)
LazyData: true
Packaged: 2014-10-28 04:56:25 UTC; arie
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-10-28 08:10:02
Package: netgsa
Type: Package
Title: Network-Based Gene Set Analysis
Version: 3.0
Date: 2016-06-15
Author: Ali Shojaie and Jing Ma
Maintainer: Jing Ma <jinma@upenn.edu>
Description: Carry out Network-based Gene Set Analysis by incorporating external information about interactions among genes, as well as novel interactions learned from data.
Depends: corpcor, Matrix, glasso, glmnet, igraph
Suggests: MASS
License: GPL (>= 2)
LazyLoad: yes
URL: http://arxiv.org/abs/1411.7919
NeedsCompilation: no
Packaged: 2016-06-16 15:41:43 UTC; jingma
Repository: CRAN
Date/Publication: 2016-06-16 18:27:48
Package: HiCfeat
Type: Package
Title: Multiple Logistic Regression for 3D Chromatin Domain Border
Analysis
Version: 1.1
Depends: R (>= 3.2.0), GenomicRanges, Matrix, glmnet, rtracklayer
Imports: IRanges, GenomeInfoDb
Date: 2016-04-27
Author: Raphael Mourad
Maintainer: Raphael Mourad <raphael.mourad@ibcg.biotoul.fr>
Description: We propose a multiple logistic regression model to assess the influences of genomic features such as DNA-binding proteins and functional elements on topological domain borders.
License: GPL-2
NeedsCompilation: no
Packaged: 2016-04-28 06:18:18 UTC; mourad
Repository: CRAN
Date/Publication: 2016-04-28 12:08:04
Package: MNS
Type: Package
Title: Mixed Neighbourhood Selection
Version: 1.0
Date: 2015-12-06
Author: Ricardo Pio Monti, Christoforos Anagnostopoulos and Giovanni Montana
Maintainer: Ricardo Pio Monti <ricardo.monti08@gmail.com>
Depends: igraph, MASS, glmnet, mvtnorm, parallel, R (>= 2.10.1)
Imports: doParallel
Description: An implementation of the mixed neighbourhood selection (MNS) algorithm. The MNS algorithm can be used to estimate multiple related precision matrices. In particular, the motivation behind this work was driven by the need to understand functional connectivity networks across multiple subjects. This package also contains an implementation of a novel algorithm through which to simulate multiple related precision matrices which exhibit properties frequently reported in neuroimaging analysis.
License: GPL-2
Packaged: 2015-12-08 11:51:56 UTC; ricardo
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-12-08 14:53:44
Package: FindIt
Version: 0.5
Date: 2015-02-27
Title: Finding Heterogeneous Treatment Effects
Author: Naoki Egami <naoki.egami5@gmail.com>, Marc Ratkovic <ratkovic@princeton.edu>, Kosuke Imai <kimai@princeton.edu>,
Maintainer: Naoki Egami <naoki.egami5@gmail.com>
Depends: R (>= 2.15.0), glmnet, lars, Matrix
Description: The heterogeneous treatment effect estimation procedure
proposed by Imai and Ratkovic (2013).
The proposed method is applicable, for
example, when selecting a small number of most (or least)
efficacious treatments from a large number of alternative
treatments as well as when identifying subsets of the
population who benefit (or are harmed by) a treatment of
interest. The method adapts the Support Vector Machine
classifier by placing separate LASSO constraints over the
pre-treatment parameters and causal heterogeneity parameters of
interest. This allows for the qualitative distinction between
causal and other parameters, thereby making the variable
selection suitable for the exploration of causal heterogeneity.
The package also contains the function, INT, which estimates
the average marginal treatment effect, the average treatment
combination effect, and the average marginal treatment interaction
effect proposed by Egami and Imai (2015).
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
Repository: CRAN
Packaged: 2015-02-27 08:31:22 UTC; naokiegami
NeedsCompilation: no
Date/Publication: 2015-02-27 12:11:22
providing 
.