Changelog#

Notable changes to sequince-density are documented in this file.

The format follows Keep a Changelog.

Unreleased #

Documentation sidebar links to the main Sequince documentation and sequince-examples under Related Packages.

First public beta release.

Normalizing-flow adapters for Zuko (PyTorch) and FlowJAX (JAX), supporting fitting, sampling, density evaluation, and forward/inverse transformations.
Gaussian-mixture proposals fitted with scikit-learn, using full covariance matrices and equal-weight training populations. Fitting and density evaluation use NumPy on the CPU.
Integration with Sequince’s FlowProposal and FlowTransform for fitted starting proposals and flow-preconditioned mutation.
Explicit RNG arguments for fitting and sampling, and checkpoint restoration of fitted models, particle transforms, and training histories. FlowJAX checkpoints also preserve the internal random key.