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.

Author
Stephan Ritter <sritter@berkeley.edu>, Alan Hubbard <hubbard@berkeley.edu>, Nicholas Jewell <jewell@berkeley.edu>
Date of publication
2015-02-25 08:12:42
Maintainer
Stephan Ritter <stephanritterRpacks@gmail.com>
License
GPL (>= 2)
Version
1.4-3
URLs

View on CRAN

Man pages

Candidates
Super learner candidates (regression methods) available for...
multiPIM
Estimate Variable Importances for Multiple Exposures and...
multiPIMboot
Bootstrap the multiPIM Function
schisto
Schistosomiasis Data Set
summary.multiPIM
Summary methods for class multiPIM
wcgs
Subset of Data from Western Collaborative Group Study

Files in this package

multiPIM
multiPIM/inst
multiPIM/inst/CITATION
multiPIM/NAMESPACE
multiPIM/data
multiPIM/data/schisto.rda
multiPIM/data/wcgs.rda
multiPIM/R
multiPIM/R/multiPIMboot.R
multiPIM/R/summary.R
multiPIM/R/multiPIM.R
multiPIM/MD5
multiPIM/DESCRIPTION
multiPIM/ChangeLog
multiPIM/man
multiPIM/man/Candidates.Rd
multiPIM/man/wcgs.Rd
multiPIM/man/summary.multiPIM.Rd
multiPIM/man/multiPIM.Rd
multiPIM/man/schisto.Rd
multiPIM/man/multiPIMboot.Rd