I've uploaded version 0.2-1 of my bibtex package to CRAN. This release anticipates changes in R 2.12.0, and structures bibtex entries in object of the new class bibentry. The release also fixes various parser and lexer bugs
I've uploaded version 0.2-1 of my bibtex package to CRAN. This release anticipates changes in R 2.12.0, and structures bibtex entries in object of the new class bibentry. The release also fixes various parser and lexer bugs
When writing a presentation we might want to use a bullet list to highlight some key points that might be lost if they are part of a large body of text. We can use the standard LaTeX environments for creating lists within a beamer presentation in a straightforward way. Fast Tube by Casper The bullet lists can
Now that the 2010 survey is over, you might be wondering what we can learn from the data when the aggregated results are published. For a good guide to the kinds of questions you'll be able to answer, take a look at StatJump, where you can see tables and charts of the results of the 2000 census: population data...
Les estivales 2010 ont commencées à montpellier.
I have recently finished reading the sixth edition of The Analysis of Time Series: An Introduction by Chatfield in our Statistics reading group. Whilst enjoying most of the book I got a little confused when looking at Appendix D: Some … Continue reading →
Andrew Gelman wrote today about some erroneous U.S. Governor approval ratings, noting that the ratings for Janet Napolitano sum to 108%. In fact most of these ratings do not sum to 100%. I prepared a clean CSV file of the ratings, making use of R‘s XML library and the readHTMLTable function. The ratings data file
In math and economics, there is a long, proud history of placing imaginary prisoners into nasty, complicated scenarios. We have, of course, the classic Prisoner’s Dilemma, as well as 100 prisoners and a light bulb. Add to that list the focus of this post, 100 prisoners and 100 boxes. In this game, the warden places
At the moment I’m working on the implementation of full block designs (e.g., every member of group A rates each member from group and vice versa. A typical example is speed dating: every man rates each woman and vice versa). These designs can be analyzed with mixed effect models, and now I’m a bit confused 