Package: fibr 0.1.1

fibr: Prior-Fraction Diagnostics for Hierarchical Models

Computes the prior fraction, the per-group pooling factor of Gelman and Pardoe (2006) <doi:10.1198/004017005000000517>, for hierarchical models, including directly from 'brms' fits. For each group-level coefficient the prior fraction is the share of the posterior precision contributed by the prior relative to the likelihood; values near one indicate a coefficient that is prior-dominated (the centring/non-centring funnel regime), values near zero indicate a likelihood-dominated coefficient that is well identified from the data. These quantities are invisible to standard convergence diagnostics such as R-hat and effective sample size, and they indicate where a non-centred reparameterisation is likely to help. A companion advisor reports the same decomposition for changepoint random effects fitted with 'smoothbp'. The underlying geometry (the Fisher-metric connection on the base-fiber split, for which this connection is flat so the obstruction is statistical rather than geometric) is described in Bindoff (2026) <doi:10.5281/zenodo.20724550>; code reproducing the paper is in the package's source repository.

Authors:Aidan D Bindoff [aut, cre]