Robust Statistics Toolkit

SkillDev tools

Robust statistical methods resistant to outliers

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Robust Statistics Toolkit skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/mathematics/skills/robust-statistics-toolkit/SKILL.md and read by ahel’s review.

Purpose

Provides robust statistical methods resistant to outliers and model violations for reliable inference.

Capabilities

M-estimators (Huber, Tukey)
Trimmed and winsorized estimators
Robust regression (MM-estimation)
Breakdown point analysis
Influence function computation
Robust covariance estimation

Usage Guidelines

Outlier Detection: Identify potential outliers first
Estimator Selection: Choose based on expected contamination
Breakdown Point: Consider required breakdown point
Efficiency: Balance robustness and efficiency

Tools/Libraries

robustbase (R)
scikit-learn
statsmodels

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: robust-statistics-toolkit
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter