Category programming

BIAC Cluster

This tutorial introduces the computational cluster at the Duke Brain Imaging and Analysis Center (BIAC) and presents a few simple use cases. Hopefully it will make your experience handling/analyzing neural...

Intro to Probabilistic Programming with Stan

In this tutorial we’re going to talk about what probabilistic programming is and how we can use it for statistical modeling. If you aren’t familiar at all with Bayesian stats,...

Machine Learning Basics

General Machine Learning

Data wrangling in the tidyverse

Today we’re going to tackle a common problem faced by graduate students: you brainstormed to design an experiment with your PI, toiled away programming the task, put it out into...

Big Data & Complexity

As human beings continue to march through the information age, we produce ever-larger quantities of data every second. This is especially relevant to us as scientists, as data gathering and...

Neural Networks

Hello! I hope you’re doing well. Today we have a tutorial on ~ neural networks ~

Intro to R and the Tidyverse

A too-brief overview of this powerful and (usually) intuitive approach to statistical computing.

Markdown hints

There are lots of powerful things you can do with the Markdown editor. If you’ve gotten pretty comfortable with writing in Markdown, then you may enjoy some more advanced tips...

The Magical Wonders of Git

Why git? git is a simple but highly flexible system for keeping track of stuff on one or more computers. But, everyone probably already has some way of doing the...

Category professional development

How To Build a Free Academic Website

with Jekyll and GitHub pages!

Blogging with R markdown and GitHub Pages

If you’re making a blog post on R-related content, you’re probably going to do it using R markdown (Rmd). However, you may have noticed that our website runs on GitHub...

Markdown hints

There are lots of powerful things you can do with the Markdown editor. If you’ve gotten pretty comfortable with writing in Markdown, then you may enjoy some more advanced tips...

Category statistics

Signal Detection, Theory and Practice

Signal detection in math and psychology

Power Analysis Through Simulation

So, you’re designing an experiment and you’re faced with answering the age-old question: How many participants do I need for this experiment to work? Probably, your advisor sent you down...

Multivariate Pattern Analysis

Why are we even here?

Big Data & Complexity

As human beings continue to march through the information age, we produce ever-larger quantities of data every second. This is especially relevant to us as scientists, as data gathering and...

Interpreting Regression Coefficients

Have you ever ran a regression and wondered where the coefficients come from or what they mean? Or perhaps you’ve tried the same analysis with different coding schemes, and the...

Bayesian Stats Basics

Bayesian statistics are gaining a whole lot of traction in psychology, neuroscience, and a whole lot of other fields. But, since most psychology departments don’t teach Bayesian statistics, you probably...

Reinforcement Learning

Our Scenario

Journal Club: Gomila (2020)

A summary and some questions for group discussion.

Category journal club

Journal Club: Gomila (2020)

A summary and some questions for group discussion.

Category machine learning

Machine Learning Basics

General Machine Learning

Neural Networks

Hello! I hope you’re doing well. Today we have a tutorial on ~ neural networks ~

Reinforcement Learning

Our Scenario

Category math

Unconvoluting convolutions - an introduction and applications to neuroscience

If you’ve worked with any kind of neural data, convolutions were involved at some point. Having an intuition for convolutions is quite useful in thinking about what your data actually...

A brief intro to the most useful kind of algebra (linear)

If you’ve ever wondered how R or python gives you regression coefficients, the answer is linear algebra! Linear algebra operations are essential to almost all modern methods for analyzing or...

Category psychology

Multivariate Pattern Analysis

Why are we even here?

Category neuroscience

Unconvoluting convolutions - an introduction and applications to neuroscience

If you’ve worked with any kind of neural data, convolutions were involved at some point. Having an intuition for convolutions is quite useful in thinking about what your data actually...

Multivariate Pattern Analysis

Why are we even here?

Category modeling

Category tutorial

Ordinal regression models to analyze Likert scale data

Today I am going to present on an alternative way to analyze Likert scale data by using ordinal regression instead of linear regression. But first, why is it even a...

Fitting drift-diffusion models with simulation

Last week, Raphael presented a fantastic conceptual introduction to drift diffusion models, which are an extension of signal detection models over time. Here I’ll be talking about what model fitting...