cTMed

Continuous Time Mediation


Keywords
centrality, continuous-time, delta-method, mediation, monte-carlo-method, network, r, r-package
License
CNRI-Python-GPL-Compatible

Documentation

cTMed

Ivan Jacob Agaloos Pesigan 2024-10-18

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Description

Calculates standard errors and confidence intervals for effects in continuous time mediation models.

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

R Core Team. (2024). 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