Are diagnostic tests included?

Yes, the skill provides specific patterns for testing normality (Jarque-Bera), heteroskedasticity (Breusch-Pagan), autocorrelation (Durbin-Watson), and identifying influential observations.

Does this skill support R-style formulas?

Yes, it includes detailed guidance and examples for using the Statsmodels Formula API (smf) to specify models using intuitive R-style strings.

Can I use this for time series forecasting?

Absolutely. The skill covers univariate and multivariate time series models including ARIMA, SARIMAX, and VAR, along with stationarity tests like ADF and PACF diagnostics.

What is the Statsmodels Claude Code skill?

It is a specialized extension for Claude Code that provides implementation patterns and best practices for using the Python statsmodels library for rigorous statistical modeling and inference.

How does this differ from scikit-learn?

While scikit-learn focuses primarily on predictive accuracy, this skill focuses on statistical inference, providing detailed p-values, confidence intervals, and tools for validating model assumptions.

Statsmodels Statistical Modeling

Name: Statsmodels Statistical Modeling
Author: pur3v4d3r

bypur3v4d3r

•

Data Science & ML

Performs rigorous statistical modeling, econometric analysis, and hypothesis testing using the Python statsmodels library.

About

Statsmodels is a powerful Claude Code skill designed for data scientists and researchers who require deep statistical inference beyond basic machine learning. It provides comprehensive tools for estimating various statistical models, conducting hypothesis tests, and performing data exploration through a range of techniques including OLS, GLM, time series analysis (ARIMA/SARIMAX), and discrete choice models. Whether you need to validate model assumptions through diagnostic tests or produce publication-ready statistical summaries, this skill offers the specialized guidance and implementation patterns needed for professional-grade econometrics and statistical analysis.

Key Features

R-style formula API support for intuitive and flexible model specification
Advanced linear regression (OLS, WLS, GLS) with robust standard errors
Comprehensive time series analysis including ARIMA, SARIMAX, and VAR models
Generalized Linear Models (GLM) for Poisson, Binomial, and Gamma distributions
1 GitHub stars
Extensive diagnostic tests for normality, heteroskedasticity, and autocorrelation

Use Cases

Estimating causal effects and performing econometric analysis on economic datasets
Validating statistical assumptions in research models through rigorous diagnostic testing
Forecasting financial or demand trends using advanced seasonal time series models

About

Key Features

R-style formula API support for intuitive and flexible model specification
Advanced linear regression (OLS, WLS, GLS) with robust standard errors
Comprehensive time series analysis including ARIMA, SARIMAX, and VAR models
Generalized Linear Models (GLM) for Poisson, Binomial, and Gamma distributions
1 GitHub stars
Extensive diagnostic tests for normality, heteroskedasticity, and autocorrelation

Use Cases

Estimating causal effects and performing econometric analysis on economic datasets
Validating statistical assumptions in research models through rigorous diagnostic testing
Forecasting financial or demand trends using advanced seasonal time series models