Introducing A Programming Tool for Bayesian Data Analysis and Simulation using R.

Our new application for the data analysis program R eliminates most of the tedium in Bayesian simulation post-processing.

Now you can draw simulations from a posterior predictive distribution with a single line of code. You can pass random arguments to already existing functions such as mean() and sum(), and obtain simulations of distributions that you can summarize simply by typing the name of a variable on the console. It is also possible to plot credible intervals of a random vector y simply by typing plot(y)…

By enabling “random variable objects” in R, summarizing and manipulating posterior simulations will be as easy as dealing with regular numerical vectors and matrices.

Read all about it in our new paper, “Fully Bayesian Computing,”.

The first beta version of the program will be soon released.

Abstract of the paper:

A fully Bayesian computing environment calls for the possibility of defining vector and array objects
that may contain both random and deterministic quantities, and syntax rules that allow treating these objects much like any variables or numeric arrays. Working within the statistical package R, we introduce a new object-oriented framework based on a new random variable data type that is implicitly represented by simulations.

We seek to be able to manipulate random variables and posterior simulation objects
conveniently and transparently and provide a basis for further development of methods and functions that can access these objects directly.

We illustrate the use of this new programming environment with several examples of Bayesian computing, including posterior predictive checking and the manipulation of posterior simulations. This new environment is fully Bayesian in that the posterior simulations can be handled directly as random variables.