After reading David Smith’s tweet on the price of Oracle R Enterprise (actually free, but it requires Oracle Data Mining at $23K/core as pointed out by Joshua Ulrich.) I went to Oracle’s site to see what was all about. Oracle … Continue reading →

After reading David Smith’s tweet on the price of Oracle R Enterprise (actually free, but it requires Oracle Data Mining at $23K/core as pointed out by Joshua Ulrich.) I went to Oracle’s site to see what was all about. Oracle … Continue reading →

Next Thursday (Jan. 16), at the RSS, there will be a special half-day meeting (afternoon, starting at 13:30) on Recent Advances in Monte Carlo Methods organised by the General Application Section. The speakers are Richard Everitt, University of Oxford, Missing data, and what to do about it Anthony Lee, Warwick University, Auxiliary variables and many-core

In the previous post we plot the Cross Validation predictions with:> plot(gas1, ncomp = 3, asp = 1, line = TRUE)We can plot the fitted values instead with:> plot(gas1, ncomp = 3, asp = 1, line = TRUE,which=train) Graphics are different:Of course, using "train" we get overoptimisc statistics and we should look...

Some of us over at McGill’s Biology Graduate Student Association have been developing and delivering R/Statistics workshops over the last few years. Through invited graduate students and faculty, we have tackled everything from multi-part introductory workshops to get your feet wet, to special topics such as GLMs, GAMs, Multi-model inference, Phylogenetic analysis, Bayesian modeling, Meta-analysis,

If you can write the likelihood function for your model, MHadaptive will take care of the rest (ie. all that MCMC business). I wrote this R package to simplify the estimation of posterior distributions of arbitrary models. Here’s how it works: 1) Define your model (ie the likelihood * prior). In this example, lets build

e-mails with the latest R posts.

(You will not see this message again.)