Nonparametric methods for smoothing regression function data with change-points, utilizing range kernels for iterative and anisotropic smoothing methods. For further details, see the paper by John R.J. Thompson (2024) <doi:10.1080/02664763.2024.2352759>.

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>.

A non-parametric test for multi-observer concordance and
    differences between concordances in (un)balanced data.

Functions for revealing what happens when effect size estimates 
    from previous studies are taken into account when evaluating each new dataset 
    in a study sequence. The analyses can be conducted for cumulative
    meta-analyses and for Bayesian data analyses. The package contains sample 
    data for a wide selection of research topics. Jointly considering 
    previous findings along with new data is more likely to result in correct 
    conclusions than does the traditional practice of not incorporating previous
    findings, which often results in a back and forth ping-pong of conclusions 
    when evaluating a sequence of studies.
    O'Connor & Ermacora (2021, <doi:10.1037/cbs0000259>).