simITS: 'simITS' package overview

In simITS: Analysis via Simulation of Interrupted Time Series (ITS) Data

Description

Analysis via Simulation of Interrupted Time Series

Details

This package is based on the backbone analytic code for the analyses in, e.g., Redcross et al. (2019) or Golub et al. (2019). See companion paper Miratrix (2020) for technical discussion of the overall approach.

Broadly, this package provides methods for fitting Interrupted Time Series 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 provides methods to visualize such data, and also to incorporate seasonality models and smoothing and aggregation/summarization. See the vignette for a guide of how to conduct such analyses.

References

Redcross, C., Henderson, B., Valentine, E. & Miratrix, L. (2019). Evaluation of pretrial justice system reforms that use the public safety assessment: Effects in Mecklenburg County, North Carolina. Technical report, MDRC (link)

Golub, C. A., Redcross, C., Valentine, E., & Miratrix, L. (2019). Evaluation of pretrial justice system reforms that use the public safety assessment: Effects of New Jersey’s criminal justice reform. Technical report, MDRC. (link)

Miratrix, L. (2020). Using Simulation to Analyze Interrupted Time Series Designs (link)

simITS documentation built on July 2, 2020, 4:10 a.m.