  A hierarchical logistic regression Bayesian model is proposed and implemented
in R to model the probability of patient improvement corresponding to any given
dosage of a certain drug. RStan is used to obtain samples from the posterior
distributions via Markov Chain Monte-Carlo (MCMC) sampling. The effects of
selecting different families of prior distributions are examined and finally,
the posterior distributions are compared across RStan, and two other
environments, namely PyMC, and AgenaRisk.
