See the general information page for workshop goals, format, what to expect, and what you need.

You can reserve your spot by signing-up to the Montreal R User Group and registering for the workshops you are interested in. The workshops are free to attend (!) and are open to everyone.

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Summer 2012

Introducing Slidify - Repoducible HTML5 Slides from R Markdown

June 28th, 2012
Dr. Ramnath Vaidyanathan (McGill, Desautels Faculty of Management)

Ramnath Vaidyanathan will introduce the group to slidify, his brand new R package.

From the slidify website:

"The objective of slidify is to make it easy to create reproducible HTML5 presentations from .Rmd files.

The guiding philosophy of slidify is to completely separate writing of content from its rendering, so that content can be written once in R Markdown, and rendered as an HTML5 presentation using any of the HTML5 slide frameworks supported."

Winter 2012

Reproducible Research with R and Sweave for Beginners

April 30, 2012
Dennis Haine (Université de Montréal)

Reproducible research was first coined by Pr. Jon Claerbout, professor of geophysics
at Stanford University, to describe that the results from researches can
be replicated by other scientists by making available data, procedures, materials
and the computational environment on which these results were produced from.

This workshop intends to describe reproducible research, what it is and why
you should care about it, and how to do it with the combination of R, LATEX,
Sweave and makefile. Tips and tricks will also be provided.

Quantile Regression

April 23, 2012
Dr. Arthur Charpentier (UQàM)

In this workshop we will examine difference concepts related to quantiles, and practical issues based on R codes.

This workshop will present quantile regression, and the idea of iterative least square estimation. It will present an illustration on climate change and hurricanes.

Introduction to Bayesian Methods

March 26, 2012
Corey Chivers (McGill)

Introduction to Bayesian reasoning, model construction, uncertainty quantification and computational techniques (Markov Chain Monte Carlo - MCMC).  This workshop is based on a package written by Corey Chivers and available on cran: MHadaptive .

Likelihood Methods

March 19, 2012
Corey Chivers (McGill)

Introduction to Likelihood theory, Maximum Likelihood Estimation (MLE), and model comparison using Information criterion (AIC). 

Plyr, reshape and other data manipulation goodies

March 12, 2012
Étienne Low-Decarie (McGill)


Like Pivot Tables on steroids.

Ever want to split your data according to factors, apply a function on each part and combine all the results into a consistent output?

Want a table of the slope for each year of your sampling data?

Want a plot for each level of your treatment?

Want to transform your data (e.g.: standardization)?

And want the high speed benefits of parallelization to boot?!

Causal Inference

March 5, 2012
Dr. Bill Shipley (Université de Sherbrooke)

Invited guest speaker on causal inference modeling.

More Mixed Models!

Feb 27, 2012

Zofia Ecaterina Taranu (McGill)

Linear, Generalized Linear, and Generalized Additive Mixed models. For all you hierarchical modeling needs!

Programming in R

Feb 6 & 13, 2012
Ben Haller (McGill)

"Never send a human to do a machine's job" - Agent Smith. Harness the awesome power of R to blast through your analysis using loops, functions, subsetting, and vectorization, while avoiding bugs and steering clear of common pitfalls in R.

Fall 2011

Intro to R - Day 1

October 3, 2011
Corey Chivers (McGill)

Opening and using R for the first time, and some basic commands.  You will also learn how to find help and figure out how to do what you want to do.

Intro to R - Day 2: Loading Data

October 11, 2011
Zofia Ecaterina Taranu (McGill)

Creating an R project: creating a script file, management of project files, housekeeping, data import (including corrupt/unusual data).

Intro to R - Day 3: Plotting

October 17, 2011
Eric Pedersen (McGill)

Graphics: How to control them and take advantage of the powerful graphing capabilities of R.

Intro to R - Day 4: Creating Data

October 24, 2011
Corey Chivers (McGill)

Simulating data is an important step in the data analysis process. In this workshop, we introduce the concept of simulation and demonstrate random variate generation from a variety of distributions and models.

Generalized Additive Models (GAMs)

November 28, 2011

Eric Pedersen (McGill)

Blending generalized linear models with additive models to fit non-conforming data.

Linear Mixed-effects Models

November 21, 2011

Cristian Correa (McGill)

Fixed and random effects linear models using Maximum Likelihood techniques with the lme4 package.

Generalized Linear Models

November 14, 2011

Paul Edwards (McGill)

Generalized Linear Models, or GLMs, allow regression and ANOVA type analysis when the standard assumptions are not met.

More Past Workshops

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