I received the following email about Introducing Monte Carlo Methods with R a few days ago: Hallo Dr. Robert, I am studying your fine book for myself. There´s a little problem in examples 7.17 and 8.1: in the R code a function “gu” is used and a reference given to ex. 5.17, but I cann´t 
The two translators of our book in Japanese, Kazue & Motohiro Ishida, contacted me about some R code mistakes in the book. The translation is nearly done and they checked every piece of code in the book, an endeavour for which I am very grateful! Here are the two issues they have noticed (after incorporating 
Jean-Michel (aka Jean-Claude!) Marin came for a few days so that we could make late progress on the revision of our book Bayesian Core towards an Use R! version. In one of the R programs in the mixture chapter, we were getting improbable answers, until we found an R mistake in the shape of which 
Boris from Ottawa sent me this email about Introducing Monte Carlo Methods with R: As I went through the exercises and examples, I believe I found a typo in exercise 6.4 on page 176 that is not in the list of typos posted on your website. For simulation of Gamma(a,1) random variables with candidate distribution 
The March 2011 issue of JASA contains a review of Introducing Monte Carlo Methods with R by Hedibert Lopes. As in the previous review, the poor quality of the figures is (rightly) pointed out by Hedie. However, the main message of the review remains very positive and, furthermore, Hedie advertises the ‘Og itself in the 
After posting so many entries about typos in my books (making you wonder how there could be any text left!) and postponing their classification for so long, I decided on Saturday afternoon to collect those entries into a comprehensive pdf document that should be more useful for readers. I incidentally noticed that my book web-page 
A casualty of cut-and-paste in Chapter 3 of Introducing Monte Carlo Methods with R. Brad McNeney from Simon Fraser sent me a nice email about the end of Example 3.6 missing a marginal estimate. Indeed, it does. And it should have been obvious from the “estimates” we derived, 19 and 16, which are not even 
![Introducing Monte Carlo Methods with R [precision]](http://www.r-bloggers.com/wp-content/uploads/2011/01/robert_casella_rbook.jpg?w=77&h=107&%23038;h=107)
Doug Rivers, professor of Political Sciences in Stanford, kindly sent me this email yesterday night: The 2nd displayed equation in section 2.1.2 on p. 44 is garbled (it might be interpreted as saying that U and X have the same distribution). I think you intended: And indeed we should have stated the implicit convention that 
David Scott wrote a review of Introducing Monte Carlo Methods with R in the International Statistical Review that is rather negative, since the main bulk reads as follows: I found some aspects of the book very disappointing. The first chapter (“Basic R Programming”) has some unfortunate mistakes and some statements, which are contentious at least 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
what’s wrong with package comment?!
I spent most of the Sunday afternoon trying to understand why defining did not have the same effect as writing the line until I found there is a clash due to the comment package… The assuredly simple code produces an error message: This is quite an inconvenience as I need to compile my solution manual
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