The goal of the rstanarm (bit.ly/rstanarm) package is to make it easier to use Bayesian estimation for most common regression models via Stan while preserving the traditional syntax that is used for specifying models in R and R packages like lme4 (bit.ly/lme4-jss). In this webinar, Ben Goodrich (bit.ly/ben-g), one of the developers of rstanarm, will introduce the most salient features of the package.
To demonstrate these features, we will fit a model to loan repayments data (bit.ly/lc-loans) from Lending Club and show why, in order to make rational decisions for loan approval or interest rate determination, we need a full posterior distribution as opposed to point predictions available in non-Bayesian statistical software.