simcausal: Simulating Longitudinal Data with Causal Inference Applications

A flexible tool for simulating complex longitudinal data using structural equations, with emphasis on problems in causal inference. Specify interventions and simulate from intervened data generating distributions. Define and evaluate treatment-specific means, the average treatment effects and coefficients from working marginal structural models. User interface designed to facilitate the conduct of transparent and reproducible simulation studies, and allows concise expression of complex functional dependencies for a large number of time-varying nodes. See the package vignette for more information, documentation and examples.

Version: 0.5.4
Depends: R (≥ 3.2.0)
Imports: data.table, igraph, stringr, R6, assertthat, Matrix, methods
Suggests: copula, tmlenet, RUnit, ltmle, knitr, ggplot2, Hmisc, mvtnorm, bindata
Published: 2017-10-08
Author: Oleg Sofrygin [aut, cre], Mark J. van der Laan [aut], Romain Neugebauer [aut]
Maintainer: Oleg Sofrygin <oleg.sofrygin at>
License: GPL-2
NeedsCompilation: no
Citation: simcausal citation info
Materials: README NEWS
CRAN checks: simcausal results


Reference manual: simcausal.pdf
Vignettes: simcausal Package: Simulations with Complex Longitudinal Data (Technical Details and Extended Examples)
Package source: simcausal_0.5.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: simcausal_0.5.4.tgz
OS X Mavericks binaries: r-oldrel: simcausal_0.5.4.tgz
Old sources: simcausal archive

Reverse dependencies:

Reverse imports: tmlenet


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