simITS: Analysis via Simulation of Interrupted Time Series (ITS) Data
Uses simulation to create prediction intervals for post-policy outcomes in interrupted time series (ITS) designs, following Miratrix (2020) <doi:10.48550/arXiv.2002.05746>. This package provides methods for fitting ITS models with lagged outcomes and variables to account for temporal dependencies. It then conducts inference via simulation, simulating a set of plausible counterfactual post-policy series to compare to the observed post-policy series. This package also provides methods to visualize such data, and also to incorporate seasonality models and smoothing and aggregation/summarization. This work partially funded by Arnold Ventures in collaboration with MDRC.
| Version: | 0.1.1 |
| Depends: | dplyr, R (≥ 2.10),rlang |
| Suggests: | arm,ggplot2,knitr,plyr,purrr,rmarkdown, stats,testthat (≥ 2.1.0),tidyr |
| Published: | 2020-05-20 |
| DOI: | 10.32614/CRAN.package.simITS |
| Author: | Luke Miratrix [aut, cre], Brit Henderson [ctb], Chloe Anderson [ctb], Arnold Ventures [fnd], MDRC [fnd] |
| Maintainer: | Luke Miratrix <lmiratrix at g.harvard.edu> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | simITS results |
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