multiPIM: Variable Importance Analysis with Population Intervention Models

Performs variable importance analysis for possibly many exposures of interest and possibly many outcomes of interest. 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: 1.2-1
Depends: lars (≥ 0.9-8), penalized, polspline, rpart
Suggests: multicore, rlecuyer
Published: 2011-11-02
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/
CRAN checks: multiPIM results

Downloads:

Package source: multiPIM_1.2-1.tar.gz
MacOS X binary: multiPIM_1.2-1.tgz
Windows binary: multiPIM_1.2-1.zip
Reference manual: multiPIM.pdf
Old sources: multiPIM archive