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/> Scandola, M., & Romano, D. (2021). <doi:10.1016/j.neuropsychologia.2021.107834>.

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 ORCID iD [aut, cre]
Maintainer: Michele Scandola <michele.scandola at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: bmscstan citation info
Materials: README
CRAN checks: bmscstan results


Reference manual: bmscstan.pdf
Vignettes: Fitting Bayesian Multilevel Single Case models using bmscstan


Package source: bmscstan_1.2.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): bmscstan_1.2.1.0.tgz, r-oldrel (arm64): bmscstan_1.2.1.0.tgz, r-release (x86_64): bmscstan_1.2.1.0.tgz, r-oldrel (x86_64): bmscstan_1.2.1.0.tgz
Old sources: bmscstan archive


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