Robust Statistics Toolkit
SkillDev toolsRobust statistical methods resistant to outliers
Use Robust Statistics Toolkit in Claude, ChatGPT or Ahel Desktop
Free. Sign in, add Robust Statistics Toolkit and connect your AI. About a minute.
Also: Claude Code · Cursor · Codex
Then ask your AI: use the Robust Statistics Toolkit skill
Details
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.
No other account needed.
Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
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
- 113
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
robust-statistics-toolkit- Source
- github.com/a5c-ai/babysitter