multiPIM (1.2-1)

Variable Importance Analysis with Population Intervention Models.

http://www.stat.berkeley.edu/users/sritter/multiPIM/
http://cran.r-project.org/web/packages/multiPIM

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.

Maintainer: Stephan Ritter
Author(s): Stephan Ritter <sritter@berkeley.edu>, Alan Hubbard <hubbard@berkeley.edu>, Nicholas Jewell <jewell@berkeley.edu>

License: GPL (>= 3)

Uses: lars, penalized, polspline, rpart, rlecuyer, multicore

Released almost 8 years ago.