In the last decades, Bayesian statistics has gained ground on the modeling of phenomena in different areas such as natural sciences, psychology, or economics. Due to several advantages such as a powerful way to quantify and propagate uncertainty, a framework for incorporating prior information and interpreting probability as a measure of confidence, or the ease to combine multiple datasets, Bayesian inference makes the fit of more complex models feasible and the meta-analysis relatively simple (Gelman et al., 2014; Korner-Nievergelt, 2015).
Despite these advantages, implementing a Bayesian framework is still difficult and restricted because the available literature generally focuses on mathematical formalism and requires greater statistical expertise than other methods (Davidson-Pilon, 2016; Jonas et al., 2013). For this reason, any tool or resource that facilitates the understanding of Bayesian statistics and its mathematical background will be very useful. Visual resources, such as graphics,