tmlenet (0.1.0)

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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.

Maintainer: Oleg Sofrygin
Author(s): Oleg Sofrygin [aut, cre], Mark J. van der Laan [aut]

License: GPL-2

Uses: assertthat, data.table, Matrix, R6, Rcpp, simcausal, speedglm, stringr, RUnit, igraph, locfit, matrixStats, foreach, doParallel, knitr
Reverse suggests: simcausal

Released almost 2 years ago.



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