Don't use Hadoop - your data isn't that big

Here.

The joy and martyrdom of trying to be a Bayesian

Here.

Use the open source R in the similar way to SAS

Here is how. Very good examples!

By the way, learning materials for the ff package are surprisingly sparse.

Three ways to run Bayesian models in R

Nice post here.

I tested the code on my machine. LaplacesDemon (LD) performs surprisingly well. For N = 20,000, JAGS takes 746 seconds, Stan takes 50 seconds (including compilation), and LD takes 42 seconds.

When N = 200,000, JAGS takes 85,44 seconds, Stan takes 280 seconds, and LD takes 382 seconds to complete. In other words, JAGS becomes impossible with "larger" data; both Stan and LD remains viable. With LD's big data capability, it is even possible that, with data size exceeds memory size, LD remains viable but Stan may not be.

It is, however, quite tricky to get LD to work properly. I spent a day tweaking a very simple linear regression model (by trying different samplers, different transformations of the data, etc.); even with very large of iterations, the demon could not be appeased.

A ggplot2 theme for Science

Here.

Using shapefiles with ggmap

Demo here.

IV regression with bootstrapped standard errors

Here.