Last night we had a great meet up of the Montreal R User Group. I got things started with a little presentation asking the question “What does R do?” (slides). I made the presentation using Montreal R User Group member Ramnath Vaidyanathan‘s Slidify package. Slidify allows you to generate rather handsome HTML5 slides directly using 
In today’s XKCD, a pair of (presumably) physicists are told by their neutrino detector that the sun has gone nova. Problem is, the machine rolls two dice and if they both come up six it lies, otherwise it tells the truth. The Frequentist reasons that the probability of obtaining this result if the sun had 
I am working on a project that requires the generation of Bernoulli outcomes. Typically, I would go about this using the built in sample() function like so: This works great and is fast, even for large n. Problem is, I want to generate each sample with its own unique probability. Seems straight forward enough, I 
After a bit of a summer lull, the Montreal R User Group is meeting up again! We’re trying out a new venue this time. Notman House is the home of the web in Montreal. They hold hackathons and other tech user group meetups, and they are all around great people in an all around great 
I recently posted the slides from a guest lecture that I gave on Bayesian methods for biologists/ecologist. In an effort to promote active learning, the class was not a straight forward lecture, but rather a combination of informational input from me and opportunities for students to engage with the concepts via activities and discussion of 
This is a talk I gave this week in Advanced Biostatistics at McGill. The goal was to provide an gentle introduction to Bayesian methodology and to demonstrate how it is used for inference and prediction. There is a link to an accompanying R script in the slides
The three benchmark algorithms for predicting the location of dark matter halos are, for the most part, all over the map. Most of the test skies look something like this: There are, however, some skies with rather strong halo signals that get a decent amount of agreement: The Lenstool MLE algorithm is the current state 
Some people like to do crossword puzzles. I like to do machine learning puzzles. Lucky for me, a new contest was just posted yesterday on Kaggle. So naturally, my lazy Saturday was spent getting elbow deep into the data. The training set consists of a series of ‘skies’, each containing a bunch of galaxies. Normally, 
If you’ve ever written code that generates a whole whack of files, you may have came across the following problem when processing them. Using a naming convention wherein files are numbered will gum up any ordering which is based on string sorting (ls, for example). What you end up with is something like this: Which 
Dispersal is a key process in many domains, and particularly in ecology. Individuals move in space, and this movement can be modelled as a random process following some kernel. The dispersal kernel is simply a probability distribution describing the distance travelled in a given time frame. Since space is continuous, it is natural to use 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
