multiPIM: Variable Importance Analysis with Population Intervention Models

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.

Version: 1.3-1
Depends: lars (≥ 0.9-8), penalized, polspline, rpart
Suggests: parallel
Published: 2013-01-18
Author: Stephan Ritter, Alan Hubbard, Nicholas Jewell
Maintainer: Stephan Ritter <sritter at berkeley.edu>
License: GPL (≥ 3)
URL: http://www.stat.berkeley.edu/users/sritter/multiPIM/
NeedsCompilation: no
CRAN checks: multiPIM results

Downloads:

Package source: multiPIM_1.3-1.tar.gz
MacOS X binary: multiPIM_1.3-1.tgz
Windows binary: multiPIM_1.3-1.zip
Reference manual: multiPIM.pdf
News/ChangeLog:ChangeLog
Old sources: multiPIM archive