Data & Analytics · PyMC

Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting.

spacering-net/codeg

Opinionated Bayesian modeling workflow with PyMC and ArviZ. An agent skill from brycewang-stanford/Auto-Empirical-Research-Skills.

brycewang-stanford/Auto-Empirical-Research-Skills

Builds, fits, checks and compares Bayesian models in PyMC, from priors and NUTS sampling to variational inference, LOO and WAIC comparison, and diagnostics.

davila7/claude-code-templates

Fit, summarize, plot, and interpret a chosen CausalPy experiment.

pymc-labs/CausalPy

Builds and checks Bayesian models with PyMC, including hierarchical models, NUTS MCMC, variational inference, mutable-data predictions, posterior predictive checks, diagnostics, and PSIS-LOO model…

K-Dense-AI/scientific-agent-skills

This skill covers Bayesian estimation and inference in quantitative social science.

brycewang-stanford/Auto-Empirical-Research-Skills

Production-grade Bayesian causal inference with PyMC, CausalPy, and DoWhy.

brycewang-stanford/Auto-Empirical-Research-Skills

Domain-validated guidance for building hierarchical Bayesian cognitive models with Stan/PyMC: prior specification, model structure, MCMC diagnostics, and posterior predictive checks

NeuroAIHub/BrainPilot

Guided statistical analysis: test choice, assumption checks, effect sizes, power, APA reporting.

jaechang-hits/SciAgent-Skills

Bayesian modeling with PyMC 5: priors, likelihood, NUTS/ADVI sampling, diagnostics (R-hat, ESS), LOO/WAIC comparison, prediction.

jaechang-hits/SciAgent-Skills

Time-to-event modeling with scikit-survival: Cox PH (elastic net), Random Survival Forests, Boosting, SVMs for censored data.

jaechang-hits/SciAgent-Skills