smolgp-dev/smolgp: Initial Release

smolgp v0.1.0 (Jan 29, 2026) This is the initial release of smolgp which includes general-use functionality, tests, and initial documentation. Highlights With v0.1.0 of smolgp, users can: Build Gaussian Process models from kernels or sums/products of kernels Condition GPs on data (which can include integrated measurements) Utilize parallel solvers on GPU Make predictions at arbitrary times Decompose multicomponent models into individual predictive components. Planned upcoming features These are in-development for a v1.0 release to match all functionality of tinygp: Sampling procedure using the state space definition. Nonzero mean functions (see Fittng a Mean Function). More expressive noise models (see tinygp.noise). Full conditioned covariance matrix as an attribute of the GP object Other upcoming features These are planned features that smolgp is uniquely capable of incorporating: Predictions at arbitrary times with arbitrary exposure times Tutorials for building models with multivariate inputs/outputs, and spatiotemporal GPs.