Marginaleffects

Marginaleffects is a Data skill for Claude Code, published by brycewang-stanford in Auto-Empirical-Research-Skills.

3.4K stars445 forkson brycewang-stanford/Auto-Empirical-Research-SkillsAdded 2026/08/16+1% in starsRepository updated 2026/08/10

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Install in seconds

Install Marginaleffects

Copy Marginaleffects into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/39-vincentarelbundock-marginaleffects ~/.claude/skills/39-vincentarelbundock-marginaleffects

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: skills/39-vincentarelbundock-marginaleffects/SKILL.md in brycewang-stanford/Auto-Empirical-Research-Skills
Installs to: ~/.claude/skills/39-vincentarelbundock-marginaleffects
Collection: One of 25 skills cataloged from this repository
Category: Data — 668 skills

What Marginaleffects does

Marginaleffects helps users interpret statistical models in R and Python via predictions, comparisons, slopes, and hypothesis tests, following the book Model to Meaning. Use it for marginal effects, treatment effects, causal inference, or function syntax.

Marginaleffects is cataloged under Data on DirSkills. Marginaleffects comes from a repository tagged academic-research, agent-skills, ai-agent, awesome-list and communication.

Documentation

README

Excerpt

marginaleffects

Primary source of information: https://marginaleffects.com Free book, case studies, and vignettes are available there.

Package manual for R and Python, plus a guide to the companion book.

Book: Model to Meaning: How to Interpret Statistical Models in R and Python

Author: Vincent Arel-Bundock (2026)
Publisher: CRC Press
Free online: https://marginaleffects.com (primary source with many case studies and vignettes)
Print: https://routledge.com/9781032908724

Core framework: Five questions for every analysis

Every interpretation task can be decomposed into five disciplined questions:

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Marginaleffects

What else does brycewang-stanford publish alongside Marginaleffects?
Marginaleffects is one of 25 skills that DirSkills catalogs from brycewang-stanford/Auto-Empirical-Research-Skills, the repository it ships in. Its siblings there include Academic Paper Composer, Academic Proofreader and Auto-Empirical Research. Each one is a separate skill with its own page in this directory, installs the same way Marginaleffects does, and is maintained by brycewang-stanford in that same repository. The rest of the collection is listed on the brycewang-stanford/Auto-Empirical-Research-Skills page.
How does Marginaleffects compare to other Data skills?
Marginaleffects ranks #203 by stars among the 668 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Marginaleffects against them. Open each page to compare what they document and how they install.