B

bayesian-statistics

Reproducible causal inference and econometrics in Python: simulations, quasi-experiments, experiments, causal ML and marketing mix models, with explicit identification assumptions and diagnostics.

R package for Gaussian processes built from first principles: composable covariance kernels, exact GP regression with Cholesky-based numerics, analytical marginal-likelihood gradients, posterior sampling, calibration diagnostics, time-series forecasting, and heteroscedastic and sparse (FITC) extensions.