tmlenet: Targeted Maximum Likelihood Estimation for Network Data

Estimation of average causal effects for single time point interventions in network-dependent data (e.g., in the presence of spillover and/or interference). Supports arbitrary interventions (static or stochastic). Implemented estimation algorithms are the targeted maximum likelihood estimation (TMLE), the inverse-probability-of-treatment (IPTW) estimator and the parametric G-computation formula estimator. Asymptotically correct influence-curve-based confidence intervals are constructed for the TMLE and IPTW. The data are assumed to consist of rows of unit-specific observations, each row i represented by variables (F.i,W.i,A.i,Y.i), where F.i is a vector of friend IDs of unit i (i's network), W.i is a vector of i's baseline covariates, A.i is i's exposure (can be binary, categorical or continuous) and Y.i is i's binary outcome. Exposure A.i depends on (multivariate) user-specified baseline summary measure(s) sW.i, where sW.i is any function of i's baseline covariates W.i and the baseline covariates of i's friends in F.i. Outcome Y.i depends on sW.i and (multivariate) user-specified summary measure(s) sA.i, where sA.i is any function of i's baseline covariates and exposure (W.i,A.i) and the baseline covariates and exposures of i's friends. The summary measures are defined with functions def.sW and def.sA. See ?'tmlenet-package' for a general overview.

Version: 0.1.0
Depends: R (≥ 3.2.0)
Imports: assertthat, data.table, Matrix, methods, R6, Rcpp, simcausal, speedglm, stats, stringr
LinkingTo: Rcpp
Suggests: doParallel, foreach, igraph, knitr, locfit, matrixStats, RUnit
Published: 2015-09-28
Author: Oleg Sofrygin [aut, cre], Mark J. van der Laan [aut]
Maintainer: Oleg Sofrygin <oleg.sofrygin at gmail.com>
BugReports: https://github.com/osofr/tmlenet/issues
License: GPL-2
URL: https://github.com/osofr/tmlenet
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: tmlenet results

Downloads:

Reference manual: tmlenet.pdf
Package source: tmlenet_0.1.0.tar.gz
Windows binaries: r-devel: tmlenet_0.1.0.zip, r-release: tmlenet_0.1.0.zip, r-oldrel: tmlenet_0.1.0.zip
OS X El Capitan binaries: r-release: tmlenet_0.1.0.tgz
OS X Mavericks binaries: r-oldrel: tmlenet_0.1.0.tgz

Reverse dependencies:

Reverse suggests: simcausal

Linking:

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