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: 1.4-1
Depends: lars (≥ 0.9-8), penalized, polspline, rpart
Suggests: parallel
Published: 2014-04-14
Author: Stephan Ritter, Alan Hubbard, Nicholas Jewell
Maintainer: Stephan Ritter <stephanritterRpacks at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: multiPIM citation info
Materials: ChangeLog
CRAN checks: multiPIM results


Reference manual: multiPIM.pdf
Package source: multiPIM_1.4-1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: multiPIM_1.4-1.tgz, r-oldrel: multiPIM_1.4-1.tgz
OS X Mavericks binaries: r-release: multiPIM_1.4-1.tgz
Old sources: multiPIM archive