multiPIM: Variable Importance Analysis with Population Intervention Models
Version 1.4-3

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.

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AuthorStephan Ritter <sritter@berkeley.edu>, Alan Hubbard <hubbard@berkeley.edu>, Nicholas Jewell <jewell@berkeley.edu>
Date of publication2015-02-25 08:12:42
MaintainerStephan Ritter <stephanritterRpacks@gmail.com>
LicenseGPL (>= 2)
Version1.4-3
URL http://www.jstatsoft.org/v57/i08/
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("multiPIM")

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

Functions

Candidates Man page
all.bin.cands Man page
all.cont.cands Man page
default.bin.cands Man page
default.cont.cands Man page
multiPIM Man page Source code
multiPIMboot Man page Source code
print.summary.multiPIM Man page Source code
schisto Man page
summary.multiPIM Man page Source code
wcgs Man page

Files

inst
inst/CITATION
NAMESPACE
data
data/schisto.rda
data/wcgs.rda
R
R/multiPIMboot.R
R/summary.R
R/multiPIM.R
MD5
DESCRIPTION
ChangeLog
man
man/Candidates.Rd
man/wcgs.Rd
man/summary.multiPIM.Rd
man/multiPIM.Rd
man/schisto.Rd
man/multiPIMboot.Rd
multiPIM documentation built on May 19, 2017, 8:06 a.m.