We just released Stan 1.1.1 and RStan 1.1.1 As usual, you can find download and install instructions at: http://mc-stan.org/ This is a patch release and is fully backward compatible with Stan and RStan 1.1.0. The main thing you should notice is that the multivariate models should be much faster and all the bugs reported for
The famous probabilist and statistician Persi Diaconis wrote an article not too long ago about the "Markov chain Monte Carlo (MCMC) Revolution." The paper describes how we are able to solve a diverse set of problems with MCMC. The first example he give...
Richard Morey writes: You and your blog readers may be interested to know that a we’ve released a major new version of the BayesFactor package to CRAN. The package computes Bayes factors for linear mixed models and regression models. Of course, I’m aware you don’t like point-null model comparisons, but the package does more than
Tyler Cowen links to a post by Sean Taylor, who writes the following about users of R: You are willing to invest in learning something difficult. You do not care about aesthetics, only availability of packages and getting results quickly. To me, R is easy and Sas is difficult. I once worked with some students
Here. Indeed, I’d much rather be a legend than a myth. I just want to clarify one thing. Walter Hickey writes: collaborated on this presentation where they take a hard look at what’s wrong with the recent trends of data visualization and infographics. The takeaway is that while there have
The other day we had a fun little discussion in the comments section of the sister blog about the appropriateness of stating forecast probabilities to the nearest tenth of a percentage point. It started when Josh Tucker posted this graph from Nate Silver: My first reaction was: this looks pretty but it’s hyper-precise. I’m a
10,000 iterations for 4 chains on the (precompiled) efficiently-parameterized 8-schools model: > date () "Thu Aug 30 22:12:53 2012" > fit3 date () "Thu Aug 30 22:12:55 2012" > print (fit3) Inference for Stan model: anon_model. 4 chains: each with iter=10000; warmup=5000; thin=1; 10000 iterations saved. mean se_mean sd 2.5% 25% 50% 75%
I was at a talk awhile ago where the speaker presented tables with 4, 5, 6, even 8 significant digits even though, as is usual, only the first or second digit of each number conveyed any useful information. A graph would be better, but even if you’re too lazy to make a plot, a bit
Josef Fruehwald writes: In the past few years, the empirical foundations of the social sciences, especially Psychology, have been coming under increased scrutiny and criticism. For example, there was the New Yorker piece from 2010 called “The Truth Wears Off” about the “decline effect,” or how the effect size of a phenomenon appears to decrease
I know next to nothing about golf. My mini-golf scores typically approach the maximum of 7 per hole, and I’ve never actually played macro-golf. I did publish a paper on golf once (A Probability Model for Golf Putting, with Deb Nolan), but it’s not so rare for people to publish papers on topics they know
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
