Internal Lab Website
Our internal site is hosted on the Open Science Foundation platform and can be found here. To access this beyond the front-facing matter, please create an OSF profile and request membership from Dr. Anderson. All our protocols, scripts, data, and other materials necessary to keep the lab running and ensure reproducibility will be housed here. If you are a student and you publish a paper with us, you can expect that your code and data will be made available on this platform.
R statistical programming
All lab members are encouraged to become familiar with either R or Python and to store their data preparation and analysis code in a GitHub repository under the CANALLAB organization (https://github.com/CANALLAB).
If you're trying to get started using R, here's a link to a workshop Dr. Anderson gave (http://rpubs.com/janderz8/125050). While this doesn't cover the tidyverse, many of the base programming commands and packages are still useful.
See also this awesome resource by Emil Hvitfeldt (@Emil_Hvitfeldt) https://emilhvitfeldt.github.io/ISLR-tidymodels-labs/index.html which they put together to accompany the book An Introduction to Statistical Learning. This resource does cover the tidyverse & is highly recommended!
Grady Lab Partial-Least-Squares User Guide for Neuroimaging data
The excellent guide developed by Cheryl Grady's Lab for analyzing and interpreting data with PLS-C using MATLAB (software available here https://www.rotman-baycrest.on.ca/index.php?section=345).
Using multivariate statistics and interpreting the results is hard, this manual is one of the clearest introductions to this topic I've come across & I still regularly refer back to it.
PLSlabManual(Feb252010) (1).pdfMultivariate analysis using the TExPosition package (guide written by Brian Nguyen)
For non-neuroimaging data or neuroimaging data that has been parcellated with an atlas, Derek Beaton and colleagues have developed an excellent set of functions in the TExPosition R package. Brian Nguyen's Bookdown walkthrough is the most comprehensive guide I've seen on this to date. I recommend taking a look through this guide if you're planning on using multivariate approaches in R.
Ihttps://bookdown.org/brian_nguyen0305/Multivariate_Statistical_Analysis_with_R/