The parameters of the beta mixed effects model can be estimated by the Bayesian approach. Bayesian inference on mixed beta models is not straightforward because the posterior distribution is not analytically available. The Markov Chain Monte Carlo (MCMC) technique is the standard approach for fitting these models cite {zuniga:2013}. In practice, this approach presents a wide range of problems in terms of convergence and computation time. Additionally, the implementation itself can be problematic, especially for end users who may not be proficient in programming. There are several software platforms for fitting generic random effects models via MCMC, including JAGS citep{Plummer03jags:a}, BayesX citep{BayesX}, and WinBUGS citep{Lunn2000}, among others. The Integrated Nested Laplace Approximation (INLA) approach is a new tool for Bayesian inference on latent Gaussian models when the focus is on posterior marginal distributions citep{ Rue2009}. INLA replaces MCMC simulations with accurate, deterministic approximations to posterior marginal distributions. A computational implementation...