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.

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
http://www.jstatsoft.org/v57/i08/

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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

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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