Ivan Jacob Agaloos Pesigan 2025-08-26
Description
Implements the methods introduced in Pesigan, Russell, and Chow (2025: https://doi.org/10.1037/met0000779) to compute standard errors, confidence intervals, and effect sizes for total, direct, and indirect effects in continuous-time mediation models.
Author-Accepted Manuscript
See https://github.com/jeksterslab/manCTMed/blob/main/.setup/latex/manCTMed-manuscript.Rtex for the latex file of the manuscript. See https://github.com/jeksterslab/manCTMed/blob/latex/manCTMed-manuscript.pdf for the compiled PDF.
Installation
You can install the development version of cTMed from GitHub with:
if (!require("remotes")) install.packages("remotes")
remotes::install_github("jeksterslab/cTMed")Documentation
See GitHub Pages for package documentation.
References
Bollen, K. A. (1987). Total, direct, and indirect effects in structural equation models. Sociological Methodology, 17, 37. https://doi.org/10.2307/271028
Deboeck, P. R., & Preacher, K. J. (2015). No need to be discrete: A method for continuous time mediation analysis. Structural Equation Modeling: A Multidisciplinary Journal, 23(1), 61–75. https://doi.org/10.1080/10705511.2014.973960
Pesigan, I. J. A., Russell, M. A., & Chow, S.-M. (2025). Inferences and effect sizes for direct, indirect, and total effects in continuous-time mediation models. Psychological Methods.
R Core Team. (2025). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
Ryan, O., & Hamaker, E. L. (2021). Time to intervene: A continuous-time approach to network analysis and centrality. Psychometrika, 87(1), 214–252. https://doi.org/10.1007/s11336-021-09767-0
Wang, L., & Zhang, Q. (2020). Investigating the impact of the time interval selection on autoregressive mediation modeling: Result interpretations, effect reporting, and temporal designs. Psychological Methods, 25(3), 271–291. https://doi.org/10.1037/met0000235