statsmodels
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
npx skills add K-Dense-AI/scientific-agent-skills --skill statsmodels --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Statsmodels: Statistical Modeling and Econometrics ## Overview Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods. Apply this skill for rigorous statistical analysis, from simple linear regression to complex time series models and econometric analyses. ## Current Compatibility Examples target statsmodels 0.14.6, released Dec 5, 2025. For reproducible environments, pin the primary package: ```bash uv pip install statsmodels==0.14.6 ``` Use `statsmodels.api` and `statsmodels.formula.api` for stable high-level imports, and direct module imports when examples require newer or specialized classes such as `HurdleCountModel`. ## When to Use This Skill This skill should be used when: - Fitting regression models (OLS, WLS, GLS, quantile regression) - Performing generalized linear modeling (logistic, Poisson, Gamma, etc.) - Analyzing discrete outcomes (binary, multinomial, count, ordinal) - Conducting time series analysis (ARIMA, SARIMAX, VAR, forecasting) - Running statistical tests and diagnostics - Testing model assumptions (heteroskedasticity, autocorrelation, normality)
- Overview
- Current Compatibility
- When to Use This Skill
- Quick Start, Capabilities, and Model Selection
- Best Practices
- Data Preparation
- Model Building
- Inference
- Model Evaluation
- Reporting
- Common Workflows
- Workflow 1: Linear Regression Analysis
- Workflow 2: Binary Classification
- Workflow 3: Count Data Analysis
uv pip install statsmodels==0.14.6 Find information about specific models rg "Quantile Regression" references/ Find diagnostic tests rg "Breusch-Pagan" references/stats_diagnostics.md Find time series guidance rg "SARIMAX" references/time_series.md
What does the statsmodels skill do?
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill statsmodels --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.