This document is a report of a final project for the Responsible Machine Learning course delivered for Ph.D. students at the University of Warsaw (Ph.D. program in Quantitative Psychology and Economics) by prof.Przemysław Biecek in the summer semester 2021.

This document generally explains the fundamental concepts of the `rmda` R package and replicates (with some modifications) some of the analyses from https://mdbrown.github.io/rmda/.

This document introduces and generally explains the Hierarchical Bayes models (BANOVA) which are particularly useful for analysis of experimental categorical repeated measures data. The modelling is done in R with BANOVA package and it replicates the analysis from section 4.3. "Application 3: Gist perception of advertising" from an article “BANOVA: An R Package for Hierarchical Bayesian ANOVA” published by Chen Dong & Michel Wedel in 2014, Journal of Statistical Software, 10(2). Hierarchical Bayes models are particularly useful for data analysis with the following features of the design: - data are collected in an experiment in which a sample of subjects have participated, and have been exposed to between-subject as well as within-subjects treatment - repeated measurements of one or more dependent variables are taken on each subject (but continuous or categorical covariates have been measured as well).