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gaussian-processes

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.

Probabilistic numerical integration framework: Bayesian Quadrature with Gaussian Process priors, classical quadrature rules, Monte Carlo methods, active sampling, and uncertainty quantification.

An Active Learning framework for Drug Discovery with the ability to create targeted models for particular experiments