bmscstan: Bayesian Multilevel Single Case Models using 'Stan'
Analyse single case analyses against a control group.
Its purpose is to provide a flexible, with good power and
low first type error
approach that can manage at the same time controls' and patient's data.
The use of Bayesian statistics allows to test both the alternative and
null hypothesis.
Scandola, M., & Romano, D. (2020, August 3). <doi:10.31234/osf.io/sajdq>
Scandola, M., & Romano, D. (2021). <doi:10.1016/j.neuropsychologia.2021.107834>.
| Version: |
1.2.1.0 |
| Depends: |
R (≥ 3.5.0), rstan, ggplot2, bayesplot |
| Imports: |
loo, logspline, LaplacesDemon |
| Suggests: |
reshape2, gridExtra, bridgesampling, testthat, knitr, rmarkdown, covr |
| Published: |
2022-09-04 |
| DOI: |
10.32614/CRAN.package.bmscstan |
| Author: |
Michele Scandola 
[aut, cre] |
| Maintainer: |
Michele Scandola <michele.scandola at univr.it> |
| BugReports: |
https://github.com/michelescandola/bmscstan |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/michelescandola/bmscstan |
| NeedsCompilation: |
no |
| Citation: |
bmscstan citation info |
| Materials: |
README |
| CRAN checks: |
bmscstan results |
Documentation:
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