Internal Validation with Bayesian models

Aki’s work is definitely the first place to look, but keep in mind the importance of the priors. If you have a flat prior for many regression coefficients you are saying that highly extreme predictions are likely. Someone else might say that’s overfitting but you would have to view it as expected given the model/prior specification. Of course that specification would be silly but it’s the default for many. I’d like to have an option in the rmsb package so that you could easily set priors on interquartile-range covariate effects, which makes things easier whether you spline variables or not.