Brandon Greenwell

doxx. Expose the contents of .docx files without leaving your terminal. Fast, safe, and smart — no Office required!

lstr. A fast, minimalist directory tree viewer, written in Rust.

fastshap. Fast approximate Shapley values in R

git-ego. Your Git identity manager and automatic profile switcher. Seamlessly manage user profiles, SSH keys, and tokens across different repositories.

pdp. A general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.

investr. Inverse estimation in R

RBitmoji. An R wrapper to the overly complicated Bitmoji API 😱

jotdown-rs. A minimalist, command-line jotting utility that's fast, private, git-friendly, and written in Rust.

MLDay18. Material from "Random Forests and Gradient Boosting Machines in R" presented at Machine Learning Day '18

uc-bana7052-old. Course materials for BANA 7052 (Applied Linear Regression) at UC

OctoAgent. A simple, experimental multi-agent AI system, built with Python, that automates triaging and fixing GitHub issues.

quarto-crc. A Quarto example of Chapman & Hall/CRC books

statlingo. Explain Statistical Output with Large Language Models

sure. Surrogate residuals for cumulative link and general regression models in R

ebm. Explainable Boosting Machines

treebook. Tree-based methods for supervised learning in R

rjournal-shapley. An Introduction to Prediction Explanations with Shapley Values

bpa. Basic pattern analysis in R

intro-ml-r. Introduction to machine learning in R (slides for Analytics Connect '18)

rstratx. An R interface to the stratx Python library

mertree. Regression trees for longitudinal and clustered data

roundhouse. An R wrapper to the ICNDb API (i.e., use R to generate random Chuck Norris facts 🙌)

ramify. Additional matrix functionality for R

is4010-course-template. Course Template for IS4010

awesome-machine-learning-interpretability. A curated list of awesome machine learning interpretability resources.

dagnammit. DAG Nammit: The Challenges and Dangers of Causally Interpreting Machine Learning Models

statlingua-py. Python

homebrew-doxx. Homebrew tap for doxx - Terminal document viewer for .docx files

dtplyr. Data table backend for dplyr

book-spark. Distributed Machine Learning in R with Apache Spark: An Introduction Using sparklyr and rsparkling

bgreenwell-blogdown. My (not so lame anymore) website...😎

SampleSHAP.jl. A Julia port of the fastshap package in R

cprint. Print common R data structures in a format which can be used as input to the interpreter

treemisc. Miscellaneous data sets and functions to accompany “Tree-Based Methods: A Practical Introduction (with Examples in R)”

pokecli. TBD

ddatools. Data sets and functions to accompany "Discrete Data Analysis for Business Analytics"

nppesapi. An R wrapper to the NPPES API

tibble. A modern re-imagining of the data frame

xgboost. Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow

uc-bana7042. UC BANA 7042: Statistical Modeling