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.