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https://github.com/jeksterslab/simstatespace

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://github.com/jeksterslab/simstatespace

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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).

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simStateSpace
================
Ivan Jacob Agaloos Pesigan

[![CRAN
Status](https://www.r-pkg.org/badges/version/simStateSpace)](https://cran.r-project.org/package=simStateSpace)
[![R-Universe
Status](https://jeksterslab.r-universe.dev/badges/simStateSpace)](https://jeksterslab.r-universe.dev/simStateSpace)
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## 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:
).

## Installation

You can install the CRAN release of `simStateSpace` with:

``` r
install.packages("simStateSpace")
```

You can install the development version of `simStateSpace` from
[GitHub](https://github.com/jeksterslab/simStateSpace) with:

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

## More Information

See [GitHub Pages](https://jeksterslab.github.io/simStateSpace) 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.

R Core Team. (2024). *R: A language and environment for statistical
computing*. R Foundation for Statistical Computing.