BayesPostEst

 Finally a modern Bayesian implementation of the Zelig package here.

Statistical Rethinking with brms, ggplot2, and the tidyverse

Amazing resource here. Great book, very exciting!

Bayesian Basics

Useful post.

Free journal articles on Bayesian analysis

Some of them are quite useful.

Bayesian instrumental variable models using brms

Here.

LaplacesDemon is back

Looks like LaplacesDemon package is back. Now we have a pure R-based Bayesian computation platform.

R Users Will Now Inevitably Become Bayesians

Good post here. I would also add that the rethinking package is a third option that helps R users to become a Bayesian.

JAGS

Looks like JAGS 4.0 is out.

Nimble 0.4 is out

Quite a number of new features. More details here.

Nimble

Just discovered this little package for Bayesian analysis. I think it is on the right direction. I hope it handle discrete parameters better than Stan.

Statistical Rethinking: A Bayesian Course with R Examples

An interesting book. The accompanied R package provides seamless integration between Stan and R.

C++ is Sugar for LaplacesDemon

This is a much anticipated change to LaplacesDemon.

MCMC in LaplacesDemon

LaplacesDemon is a really cool package but I had a hard time to grasp its full capacity. This article summarizes provides a nice summary.

Big data and Bayesian inference

This post provides invaluable information about the use of Bayesian method in big data analysis. I did not realize before that the LapLacesDemon package has provides such a powerful and versatile environment for Bayesian inference.

Right now the package works at fairly low level. The user needs to specify the likelihood function and take care of various kinds of details. A wrapper package that provides a simplified interface and reasonable default options will be an excellent addition and probably will help attracting a much wider use base.

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.

Stan as a unified statistical estimation and intepretation engine

Applied researchers who are used to Stata or R need a reason to learn and use Stan. Besides the usual Bayesian vs. frequentist discussions, there is also a practical one.

Stan provides a unified interface for statistical estimation and interpretation. I have been using R with the Zelig package for estimation and interpretation during the past years, which is great. The problem is that, because Zelig is built upon a large number of existing R packages written by many different researchers and the quality of these packages vary greatly, working with Zelig means that you are working with all these other packages and researchers as well. In addition, even though in theory you can modify the source of these packages to suite your needs, but applied researches rarely have the energy or skills to tweak the FORTRAN or C code.

Stan provides a modeling language, which makes it easy for user to tweak their model (of course the underlying C++ code is also available). It is simulation-based and uses posterior distribution for inference, which means that there is no need for an additional simulation step (as what Zelig brings to frequentist models). After some testing, I have come to the conclusion that Stan is fast and stable enough for my daily data analysis work.

The best of all, this package comes from a research group with very good reputation and their discussion list is unbelievably helpful.

These are good enough reasons for me to switch to Stan.

Case Studies in Bayesian Statistical Modelling and Analysis

This book looks interesting.

Bayesian articles

This is a set of helpful articles on the historical development of Bayesian statistics.

Applied Bayesian Statistics

This book looks interesting.

Stan code for my favorite book

I am learning Stan. So I decided to translate the BUGS code for my favorite applied Bayesian book into  Stan code. I just did the translation for a couple of chapters and I feel I know Stan much better already.