Approximate Bayesian inference and Monte Carlo validation for
small-N repeated-measures designs with two time points and two raters.
The package is intended for applications in which sample size is limited
and the observed outcome may be affected by rater-specific bias.
User-supplied data are standardised into a common long-format structure.
Pre-post effects are analysed using difference scores in a linear model
with a rater indicator as covariate. Posterior summaries for the
regression coefficients are obtained from a large-sample normal
approximation centred at the least-squares estimate with plug-in
covariance under a flat improper prior. Evidence for a non-zero
pre-post effect, adjusted for rater differences, is summarised using a
BIC-based approximation to the Bayes factor for comparison between
models with and without the pre-post effect. Monte Carlo validation uses
design quantities estimated from the observed data, including sample
size, mean pre-post change, and second-rater additive discrepancy, and
summarises inferential performance in terms of bias, root mean squared
error, credible interval coverage, posterior tail probabilities, and
mean Bayes factor values. For background on the BIC approximation and
Bayes factors, see Schwarz (1978)