Perform linear, nonlinear, and polynomial regression with confidence intervals and prediction bounds.
npx skills add https://github.com/jkitchin/skillz --skill pycse
Please help me install this Agent Skill. Skill: pycse Source: https://github.com/jkitchin/skillz/tree/main/skills/python/pycse Command: npx skills add https://github.com/jkitchin/skillz --skill pycse
This Skill provides wrappers around SciPy for regression tasks with automatic confidence intervals and prediction bounds, dramatically reducing boilerplate and error-prone math.
Use pycse.nlinfit to fit a nonlinear model and obtain confidence intervals with a single call.
High-intent search queries and answers about installing and using this skill.
Regression with confidence intervals involves fitting a model to data and calculating uncertainty bounds around parameters. pycse automates this by wrapping SciPy functions to return parameter estimates, interval bounds, and standard errors in a single call for linear, nonlinear, and polynomial models.
Prediction intervals quantify uncertainty around individual forecasts from a fitted model. pycse provides prediction interval bounds alongside point estimates, enabling you to report ranges rather than single values when predicting new data points.
Model comparison uses metrics like BIC and R² to evaluate which model better explains your data. pycse exposes these statistics directly from fitted models, letting you rank candidate fits without manual calculations.
Yes. pycse.nlinfit handles nonlinear regression alongside linear and polynomial cases, all returning confidence intervals and prediction bounds through the same consistent API.
Caching mechanisms like HashCache, JsonCache, and SqlCache store computation results, avoiding redundant fitting when you re-run analysis pipelines. pycse integrates these to persist expensive regressions.
pycse supports direct data ingestion from Google Sheets, eliminating manual export steps and keeping your analysis workflow connected to live data sources.
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