Mici: Manifold Markov chain Monte Carlo methods in Python

Major release. Dependency changes Dropped support for Python 3.6, 3.7 and 3.8. Minimum NumPy version changed to 1.22 per NEP 29. API changes Interface to sampler classes simplified to remove previous sample_chain and sample_chains_with_adaptive_warm_up methods in favour of combining all functions into a single sample_chains method, which now requires arguments to be passed specifying both number of warm-up iterations (n_warm_up_iter, can be zero) and number of main iterations (n_main_iter). All arguments to sample_chain other than n_warm_up_iter, n_main_iter and init_states are now keyword only. metric and optional arguments specifying derivatives (for example grad_neg_log_dens and jacob_constr) to system classes are now all keyword only. sample_chains methods now return named tuples with entries final_states, traces and statistics. memmap_enabled keyword argument to sample_chains removed and replaced with force_memmap argument, with adjusted semantics that memory mapping is now always enabled when sampling chain in parallel on multiple processes, with force_memmap allowing memory mapping to also be used when sampling a chain or chains on a single process. Bug fixes Bug due to mici.transitions.Transition.statistic_types being shared across subclasses as a mutable set fixed. Accept probabilities now computed using exp(min(.)) rather than min(exp(.)) to improve numerical stability. reverse_norm argument to ConstrainedLeapfrogIntegrator initializer was previously ignored - this is now fixed. Usages of deprecated numpy.bool type removed. New, changed and removed features Added implicit midpoint integrator (mici.integrators.ImplicitMidpointIntegrator) for non-separable Hamiltonian systems, with dh2_dpos method added to EuclideanMetricSystem to allow also using with implicit midpoint integrator for testing purposes. Added family of symmetric composition integrators described in Blanes, Casas, Sanz-Serna (2014). Added option to record traces during warm-up (trace_warm_up argument to sample_chains method). Added new mici.interop module with convenience functions for converting Mici sample_chains output to an arviz.InferenceData object, sampling a PyMC model with Mici and sampling a PyStan model with Mici. Added new projection solver for use with ConstrainedLeapfrogIntegrator which performs a Newton iteration with backtracking line search (mici.solvers.solve_projection_onto_manifold_newton_with_line_search). Default for projection_solver argument to ConstrainedLeapfrogIntegrator initializer changed to mici.solvers.solve_projection_onto_manifold_newton. Step size adapter now allows using custom reducer for combining per-chain adapted step sizes. Current step size now recorded in integration transition statistics (with key step_size) allowing live monitoring using monitor_stats argument to sample_chains. Handling of matrices initialised with non finite values improved with new mici.errors.LinAlgError exception being raised. Automatic chain truncation on keyboard interrupt removed. Norm functions now do not explicitly use NumPy API, instead using array methods / operator overloads, to allow using to array-like inputs such as jax.Array instances. Documentation and packaging Documentation now built using Sphinx. Additional details and references added to docstrings and formatting improved. Project metadata now stored in pyproject.toml file with setup.py script removed.If you use this software, please cite it as below