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nonstat  

Detecting Nonstationarity in Time Series
View on CRAN: Click here


Download and install nonstat package within the R console
Install from CRAN:
install.packages("nonstat")

Install from Github:
library("remotes")
install_github("cran/nonstat")

Install by package version:
library("remotes")
install_version("nonstat", "0.0.6")



Attach the package and use:
library("nonstat")
Maintained by
Martin Hecht
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-04-01
Latest Update: 2025-04-01
Description:
Provides a nonvisual procedure for screening time series for nonstationarity in the context of intensive longitudinal designs, such as ecological momentary assessments. The method combines two diagnostics: one for detecting trends (based on the split R-hat statistic from Bayesian convergence diagnostics) and one for detecting changes in variance (a novel extension inspired by Levene's test). This approach allows researchers to efficiently and reproducibly detect violations of the stationarity assumption, especially when visual inspection of many individual time series is impractical. The procedure is suitable for use in all areas of research where time series analysis is central. For a detailed description of the method and its validation through simulations and empirical application, see Zitzmann, S., Lindner, C., Lohmann, J. F., & Hecht, M. (2024) "A Novel Nonvisual Procedure for Screening for Nonstationarity in Time Series as Obtained from Intensive Longitudinal Designs" <https://www.researchgate.net/publication/384354932_A_Novel_Nonvisual_Procedure_for_Screening_for_Nonstationarity_in_Time_Series_as_Obtained_from_Intensive_Longitudinal_Designs>.
How to cite:
Martin Hecht (2025). nonstat: Detecting Nonstationarity in Time Series. R package version 0.0.6, https://cran.r-project.org/web/packages/nonstat. Accessed 19 Apr. 2025.
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