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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=simITS to link to this page.
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