Resources

minimalboxplot

Create minimal boxplots as described in Tufte (2001) p.125. [Github ]

asmap

Lightweight method for loading in various filetypes as map in Go. To use, install via go get github.com/w-decker/asmap@latest. [GitHub ]

olsgo

Ordinary-least-squares bivariate regression in Go. Lightweight and intuitive modular functions for step-wise approach or one-time function to run regression. Offers simple CSV reading (as map) and some default plotting options (raw data and model). To use, install via go get github.com/w-decker/olsgo@latest. [Github ]

Readinglist

Getting recently added items from a Zotero library. Still in progress; this is a locally executable demo. [GitHub ]

roipy

Plotting brain regions of interest (ROI) for demonstration purposes in Python. Wrapped around Nilearn. [PyPi ]

Static Functional Connectivity

Execution of static functional connectivity analyses with nilearn to scaffold later pipelines. Equipped with custom module, sfc_demo, which contains different functionality for easily demonstrating static functional connectivity analyses. [GitHub ]

APA $\LaTeX$ Template

LaTeX and Overleaf are great resources for (collaboratively) writing documents with ease. There are a million tutorials on YouTube. I recommend checking out Dr. Trefor Bazett. Nonetheless, I have provided a template which follows APA criteria. [GitHub ]

Python Object Oriented Programming (POOP)

Object oriented programming (OOP) is an interesting way of coding and is difficult compared to the canonical “procedural” way of programming. Here, I provide only an introductory explanation of OOP with Python. Thus, this learning tool is aptly named “POOP”. [GitHub ]

Multiple regression subcomponents

R functions for computing some fun components to multiple regression models. Clicking the plot above will take you to the GitHub repo. To install mrc in R, type devtools::install_github("w-decker/mrc") in the R console. [GitHub ]

Brain Stimulation with Izhikevich Model

The Izhikevich Model, developed in 2003, allows us to quantify the dynamic nature of neuronal circutry. Here, I provide an introduction to the mathematical models behind neuronal activity and using this to understand brain stimulation. [GitHub ]

Intro to hidden Markov models

What is a Hidden Markov Model (HMM)? How is it used in cognitive neuroscience? Here I provide an understanding of the concepts underlying HMMs, HMMs in general, and relevant literature that makes use of Markovian processes. [Website ]

Building MATLAB Functions

MATLAB is a powerful and widely used tool in cognitive neuroscience and building custom functions is important to optimizing your code. Here, I provide an understanding of building functions ranging in complexities. [Website ]