simStateSpace: Simulate Data from State Space Models

Ivan Jacob Agaloos Pesigan

Description

Provides a streamlined and user-friendly framework for simulating data in state space models, particularly when the number of subjects/units ($n$) exceeds one, a scenario commonly encountered in social and behavioral sciences. For an introduction to state space models in social and behavioral sciences, refer to Chow, Ho, Hamaker, and Dolan (2010: https://doi.org/10.1080/10705511003661553).

Installation

You can install the CRAN release of simStateSpace with:

install.packages("simStateSpace")

You can install the development version of simStateSpace from GitHub with:

if (!require("remotes")) install.packages("remotes")
remotes::install_github("jeksterslab/simStateSpace")

More Information

See GitHub Pages for package documentation.

References

Chow, S.-M., Ho, M. R., Hamaker, E. L., & Dolan, C. V. (2010). Equivalence and differences between structural equation modeling and state-space modeling techniques. Structural Equation Modeling: A Multidisciplinary Journal, 17(2), 303–332. https://doi.org/10.1080/10705511003661553

Chow, S.-M., Losardo, D., Park, J., & Molenaar, P. C. M. (2023). Continuous-time dynamic models: Connections to structural equation models and other discrete-time models. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (2nd ed.). The Guilford Press.

R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

Shumway, R. H., & Stoffer, D. S. (2017). Time series analysis and its applications: With R examples. Springer International Publishing. https://doi.org/10.1007/978-3-319-52452-8

Uhlenbeck, G. E., & Ornstein, L. S. (1930). On the theory of the brownian motion. Physical Review, 36(5), 823–841. https://doi.org/10.1103/physrev.36.823