Efficient Sequential Testing with Evidence Ratios
Computes the Akaike Information Criterion
Analysing the results of simulations ran with simER
Computes the Bayesian Information Criterion
Simulating many sequential testing with evidence ratios and plotting t...
Efficient Sequential Testing with Evidence Ratios
Computes Akaike weights or pseudo-BMA weights for a set of models
Plotting the results of simER
Computes sequential evidence ratios
Computes sequential evidence ratios for a given data set and permutati...
Simulates sequential testing with evidence ratios
An implementation of sequential testing that uses evidence ratios computed from the weights of a set of models. These weights correspond either to Akaike weights computed from the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC) and following Burnham & Anderson (2004, <doi:10.1177/0049124104268644>) recommendations, or to pseudo-BMA weights computed from the WAIC or the LOO-IC of models fitted with 'brms' and following Yao et al. (2017, <arXiv:1704.02030v3>).