ASKurz/Doing-Bayesian-Data-Analysis-in-brms-and-the-tidyverse: correct, refine, and augment

Noteworthy changes include:

• using the Metropolis algorithm to fit the bivariate Bernoulli model for Figure 7.6 (Section 7.4.3);
• corrections to mistakes around the lag() and lead() functions in Section 7.5.2;
• an added bonus section clarifying the pooled standard deviation for standardized mean differences (Section 16.3.0.1);
• refining the custom stat_wilke() plotting function in Chapter 18;
• an overhaul of the bonus section covering effect sizes (Section 19.6);
• refining/correcting the threshold workflow for univariable logistic regression models (Chapter 21);
• fixing the divergent transitions issue for the robust logistic regression model by adding boundaries on the prior (Section 21.3);
• corrections to a few incorrectly computed effect sizes in Chapter 23;
• the addition of a new bonus section (Section 22.3.3.1.1) highlighting the benefits of the intercepts-only softmax model;
• expansions to the material on censored data (Section 25.4) and the addition of a brief introduction to truncated data (Section 25.4.4); and
• updating all HMC fits to the current version of brms (2.15.0).