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: |
0.5.7 |
| Depends: |
R (≥ 3.2.0) |
| Imports: |
data.table, igraph, stringr, R6, assertthat, Matrix, methods |
| Suggests: |
copula, RUnit, ltmle, knitr, ggplot2, Hmisc, mvtnorm, bindata |
| Published: |
2024-10-19 |
| DOI: |
10.32614/CRAN.package.simcausal |
| Author: |
Oleg Sofrygin [aut],
Mark J. van der Laan [aut],
Romain Neugebauer [aut],
Fred Gruber [ctb, cre] |
| Maintainer: |
Fred Gruber <fgruber at gmail.com> |
| BugReports: |
https://github.com/osofr/simcausal/issues |
| License: |
GPL-2 |
| URL: |
https://github.com/osofr/simcausal |
| NeedsCompilation: |
no |
| Citation: |
simcausal citation info |
| Materials: |
README NEWS |
| CRAN checks: |
simcausal results |
Documentation:
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