Uses simulation to create prediction intervals for post-policy outcomes in interrupted time series (ITS) designs, following Miratrix (2020) <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 |
| 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 |
| Reference manual: | simITS.pdf |
| Vignettes: |
Intro simITS |
| Package source: | simITS_0.1.1.tar.gz |
| Windows binaries: | r-devel: simITS_0.1.1.zip, r-release: simITS_0.1.1.zip, r-oldrel: simITS_0.1.1.zip |
| macOS binaries: | r-release: simITS_0.1.1.tgz, r-oldrel: simITS_0.1.1.tgz |
| Old sources: | simITS archive |
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