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 |
Documentation:
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