Package: monomvn
Type: Package
Title: Estimation for Multivariate Normal and Student-t Data with
Monotone Missingness
Version: 1.9-6
Date: 2016-02-10
Author: Robert B. Gramacy <rbgramacy@chicagobooth.edu>
Maintainer: Robert B. Gramacy <rbgramacy@chicagobooth.edu>
Description: Estimation of multivariate normal and student-t data of
arbitrary dimension where the pattern of missing data is monotone.
Through the use of parsimonious/shrinkage regressions
(plsr, pcr, lasso, ridge, etc.), where standard regressions fail,
the package can handle a nearly arbitrary amount of missing data.
The current version supports maximum likelihood inference and
a full Bayesian approach employing scale-mixtures for Gibbs sampling.
Monotone data augmentation extends this
Bayesian approach to arbitrary missingness patterns.
A fully functional standalone interface to the Bayesian lasso
(from Park & Casella), Normal-Gamma (from Griffin & Brown),
Horseshoe (from Carvalho, Polson, & Scott), and ridge regression
with model selection via Reversible Jump, and student-t errors
(from Geweke) is also provided.
Depends: R (>= 2.14.0), pls, lars, MASS
Imports: quadprog, mvtnorm
License: LGPL
URL: http://bobby.gramacy.com/r_packages/monomvn
NeedsCompilation: yes
Packaged: 2016-02-10 21:11:47 UTC; bobby
Repository: CRAN
Date/Publication: 2016-02-11 00:43:05
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: FeaLect
Type: Package
Title: Scores Features for Feature Selection
Version: 1.10
Date: 2014-12-03
Author: Habil Zare
Maintainer: Habil Zare <zare@txstate.edu>
Depends: lars, rms
Description: For each feature, a score is computed that can be useful
for feature selection. Several random subsets are sampled from
the input data and for each random subset, various linear
models are fitted using lars method. A score is assigned to
each feature based on the tendency of LASSO in including that
feature in the models.Finally, the average score and the models
are returned as the output. The features with relatively low
scores are recommended to be ignored because they can lead to
overfitting of the model to the training data. Moreover, for
each random subset, the best set of features in terms of global
error is returned. They are useful for applying Bolasso, the
alternative feature selection method that recommends the
intersection of features subsets.
License: GPL (>= 2)
LazyLoad: yes
Repository: CRAN
Date/Publication: 2015-05-13 00:55:14
Packaged: 2015-05-12 17:36:25 UTC; habil
NeedsCompilation: no
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
Package: RXshrink
Title: Maximum Likelihood Shrinkage via Generalized Ridge or Least
Angle Regression
Version: 1.0-8
Date: 2014-1-13
Author: Bob Obenchain <wizbob@att.net>
Maintainer: Bob Obenchain <wizbob@att.net>
Depends: R (>= 1.8.0), lars
Description: Identify and display TRACEs for a specified shrinkage path and determine
the extent of shrinkage most likely, under normal distribution theory, to produce an
optimal reduction in MSE Risk in estimates of regression (beta) coefficients.
License: GPL (>= 2)
URL: http://www.r-project.org
Packaged: 2014-01-14 04:43:55 UTC; wiz
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-01-14 07:14:08
Package: GPC
Type: Package
Title: Generalized Polynomial Chaos
Version: 0.1
Depends: R (>= 2.7.0), randtoolbox, orthopolynom, ks, lars
Date: 2013-02-01
Author: Miguel Munoz Zuniga and Jordan Ko
Maintainer: Miguel Munoz Zuniga<miguel.munoz-zuniga@ifpen.fr>
Description: A generalized polynomial chaos expansion of a model taking as input independent random variables is achieved. A statistical and a global sensitivity analysis of the model are also carried out.
License: GPL-3
Packaged: 2014-12-18 17:17:50 UTC; munozzum
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-12-18 21:08:58
Package: elasticnet
Version: 1.1
Date: 2012-06-25
Title: Elastic-Net for Sparse Estimation and Sparse PCA
Author: Hui Zou <hzou@stat.umn.edu> and Trevor Hastie
<hastie@stanford.edu>
Maintainer: Hui Zou <hzou@stat.umn.edu>
Depends: R (>= 2.10), lars
Description: This package provides functions for fitting the entire
solution path of the Elastic-Net and also provides functions
for estimating sparse Principal Components. The Lasso solution
paths can be computed by the same function. First version:
2005-10.
License: GPL (>= 2)
URL: http://www.stat.umn.edu/~hzou
Packaged: 2012-06-28 05:33:46 UTC; emeryyi
Repository: CRAN
Date/Publication: 2012-06-28 08:57:54
providing 
.