Fit statistical models and produce inference and diagnostics for tabular datasets.
npx skills add https://github.com/Clay-HHK/claude-config --skill statsmodels-clay-hhk
Please help me install this Agent Skill. Skill: statsmodels Source: https://github.com/Clay-HHK/claude-config/tree/main/skills/statsmodels Command: npx skills add https://github.com/Clay-HHK/claude-config --skill statsmodels-clay-hhk
Provide rigorous estimation, inference, and diagnostics for statistical models so analysts can make valid conclusions, compare specifications, and produce reproducible forecasts without ad-hoc scripting.
Fit an OLS model with robust standard errors and produce a formatted coefficient table, p-values, confidence intervals, and residual diagnostics for the dataframe df with outcome y and predictors x1 and x2.
High-intent search queries and answers about installing and using this skill.
Fit OLS regression with robust standard errors by applying the formula API to your tabular dataset, yielding coefficient estimates, p-values, confidence intervals, and residual diagnostics.
Yes, you can model count data using Poisson, Negative Binomial, and zero-inflated models, producing coefficient estimates, marginal effects, and AIC/BIC comparisons for valid inference.
Time series forecasting is supported through ARIMA, SARIMAX, VAR, and state space models, generating forecasts with prediction intervals for econometric and biostatistical workflows.
Compare statistical model specifications using AIC/BIC metrics and likelihood-ratio tests, evaluating residual outputs and influence diagnostics to ensure valid econometric conclusions.
Estimate generalized linear models including logit, probit, and MNLogit for discrete choice analysis, returning coefficient estimates, marginal effects, and clustered standard errors.
Use quantile regression instead of OLS when modeling conditional quantiles is necessary, producing robust coefficient estimates and inference outputs for tabular datasets.
dotlab-hqPerform GPU-accelerated groupby aggregations and statistical summaries on large CSV datasets.
jasrajtulsiFit statistical models and run diagnostics in Python with statsmodels.
Axel-DaMageAutomate pandas DataFrame operations for cleaning, aggregation, and time series analysis.
Estrella-231Build Bayesian models with PyMC and infer posterior distributions.
