Can I use R-style formulas with this skill?

Yes, this skill fully supports the Statsmodels formula API, allowing you to define complex models using familiar R-style syntax like 'y ~ x1 + x2'.

Does it support time series forecasting?

Absolutely. It provides patterns for classical time series methods including ARIMA and SARIMAX, along with stationarity tests like the Augmented Dickey-Fuller (ADF) test.

When should I use this skill instead of aeon?

Use Statsmodels for classical statistical time series analysis and inference. Use the aeon skill for modern machine-learning-based forecasting, clustering, and anomaly detection.

How does this differ from the scikit-learn skill?

While scikit-learn focuses on predictive machine learning pipelines, Statsmodels is optimized for statistical inference, providing detailed p-values, confidence intervals, and rigorous diagnostic tests.

Statsmodels Statistical Analysis

Name: Statsmodels Statistical Analysis
Author: andikarachman

byandikarachman

•

数据科学与机器学习

Provides expert patterns for rigorous statistical modeling, econometrics, and hypothesis testing within Claude Code.

This skill equips Claude with specialized knowledge for Python's Statsmodels library, enabling precise estimation, inference, and diagnostics. It is essential for workflows requiring classical statistical methods like OLS, GLM, and ARIMA time series analysis, where p-values, confidence intervals, and model assumptions are critical. Whether performing Exploratory Data Analysis (EDA) or designing complex experiments, this skill ensures robust results through R-style formulas and comprehensive diagnostic testing to validate model integrity.

主要功能

01Support for R-style formula API for intuitive and readable model specification

02Comprehensive time series tools including ARIMA, SARIMAX, and VAR models

03Generalized Linear Models (GLM) for binary, count, and skewed data analysis

04Advanced regression modeling including OLS, WLS, and Quantile regression

05Rigorous diagnostic tests for stationarity, normality, and heteroskedasticity

069 GitHub stars

使用场景

01Forecasting time-ordered data with seasonal components and exogenous variables

02Conducting econometric analysis to estimate causal effects in observational data

03Validating model assumptions and detecting influential outliers in research data

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add andikarachman/data-science-plugin statsmodels

For use in Claude.ai and ChatGPT

主要功能

01Support for R-style formula API for intuitive and readable model specification

02Comprehensive time series tools including ARIMA, SARIMAX, and VAR models

03Generalized Linear Models (GLM) for binary, count, and skewed data analysis

04Advanced regression modeling including OLS, WLS, and Quantile regression

05Rigorous diagnostic tests for stationarity, normality, and heteroskedasticity

069 GitHub stars

使用场景

01Forecasting time-ordered data with seasonal components and exogenous variables

02Conducting econometric analysis to estimate causal effects in observational data

03Validating model assumptions and detecting influential outliers in research data

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add andikarachman/data-science-plugin statsmodels

For use in Claude.ai and ChatGPT