Pinned Repositories
apa
Format output of statistical tests in R according to APA guidelines
arm-bayes-2020
attentional_snarc
Repository for Registered Replication Report on Fischer, Castel, Dodd, and Pratt (2003)
bayes2022
BayesFactor
Simple Rust/C code for computing one-sample Default Bayesian T tests in Matlab
bayesplay
The Bayesian playground. A package for learning Bayes factors
chronicler
Fischer-2003-Replication
PsychToolBox code the replication of Fischer et al 2003
FischerRRR-eyetracking
FischerRRR PTB code for EyeLink 1000
termpdf
termpdf
ljcolling's Repositories
ljcolling/termpdf
termpdf
ljcolling/extendr
R extension library for rust designed to be familiar to R users.
ljcolling/paas22
ljcolling/rust_stats
Rust implementation of R's stats module.
ljcolling/sustainable-computing
Website for Green Research Computing for the Health and Life Sciences
ljcolling/aerial.nvim
Neovim plugin for a code outline window
ljcolling/AIX360
Interpretability and explainability of data and machine learning models
ljcolling/bayesCorr
ljcolling/bayesplay-rewrite
bayesplay
ljcolling/bed-reader
A library for easy, fast, and efficient reading & writing of PLINK Bed files
ljcolling/BIMT
Brain-Inspired Modular Training (BIMT), a method for making neural networks more modular and interpretable.
ljcolling/cats
CATS: the Climate-Aware Task Scheduler :cat2: :tiger2: :leopard:
ljcolling/climate-informatics-repo-challenge-talk
Talk for the Climate Informatics Reproducibility Challenge
ljcolling/codecarbon
Track emissions from Compute and recommend ways to reduce their impact on the environment.
ljcolling/cuarfc
ljcolling/faux
R functions for simulating factorial datasets
ljcolling/libR-sys
Low level R bindgen interface
ljcolling/lime
Lime: Explaining the predictions of any machine learning classifier
ljcolling/llama.cpp
Port of Facebook's LLaMA model in C/C++
ljcolling/matlab-arm
ljcolling/niceQuiz
learnr style quizzes in plain javascript
ljcolling/nvim_config
ljcolling/ordtools
ordtools
ljcolling/research-next-rewrite
research
ljcolling/responsible-ai-toolbox
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
ljcolling/reward-reports
Documentation for dynamic machine learning systems.
ljcolling/shap
A game theoretic approach to explain the output of any machine learning model.
ljcolling/tewt
ljcolling/tractjs
Run ONNX and TensorFlow inference in the browser.
ljcolling/what-if-tool
Source code/webpage/demos for the What-If Tool