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 gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://www.jstatsoft.org/v57/i08/ |
| 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: multiPIM_1.4-1.zip, r-release: multiPIM_1.4-1.zip, r-oldrel: multiPIM_1.4-1.zip |
| 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 |