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. Software Depends: lars (>= 0.9-8), penalized, polspline, rpart Suggests: multicore, rlecuyer Stephan Ritter <sritter@berkeley.edu> Stephan Ritter <sritter@berkeley.edu>, Alan Hubbard <hubbard@berkeley.edu>, Nicholas Jewell <jewell@berkeley.edu> GPL (>= 3) 2011-11-02 application/tgz http://CRAN.R-project.org/package=multiPIM