marginaleffects
CommunityClarify model results with clear marginal effects
Authorvincentarelbundock
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill translates opaque model outputs into clear, actionable marginal-effect insights for data analysts across R and Python.
Core Features & Use Cases
- Convert model results into marginal effects, including predictions, contrasts, slopes, and hypothesis tests.
- Provide unit-level (conditional) and average (marginal) estimates for a wide range of estimands (ATE, ATT, CATE, risk/odds ratios, etc.).
- Support exploratory grids and counterfactual scenarios via datagrid-like operations to explore predictor spaces.
Quick Start
Install and load the marginaleffects Skill, then ask for guidance on interpreting predictions, contrasts, and slopes from your model results.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: marginaleffects Download link: https://github.com/vincentarelbundock/marginaleffects/archive/main.zip#marginaleffects Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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