Particles

Blog post: TBA
Added
- nested: NS-SMC sampler of Salomone et al (2018)
- datasets: Liver
- distributions: LogNormal
- distributions: Mixture, FlatNormal, mixMissing (to deal with missing data)
- distributions: VaryingCovNormal (issue 55 on github)
- smoothing: FFBS-MCMC, FFBS-hybrid
- collectors: Paris algorithm (hybrid version)
- smc_samplers: single-run variance estimates
- smc_samplers: Tempering (fixed exponents)
- smc_samplers: AdaptiveTempering has a new argument, max_iter, to put a cap on the number of iterations

Blog post: <https://statisfaction.wordpress.com/2021/10/25/particles-0-3-waste-free-smc-fortran-dependency-removed-binary-spaces/>
Added
- new module: binary_smc
- smc_samplers: waste-free SMC (now default)
- resampling: added killing resampling scheme
- new tutorial notebook: how to define complicated state-space models

Changed
- qmc: now based on scipy.stats.qmc (remove Fortran code dependency)

Blog post: <https://statisfaction.wordpress.com/2021/01/29/particles-0-2-whats-new-whats-next-your-comments-most-welcome/>
Added
- new module: datasets (cleaner way to load standard datasets)
- new module: variance_estimators (single-run genealogy based estimators à la
Lee and Whiteley)

Changed
- collectors: new implementation (breaks compatibility)
- utils: performance improvements (multi-processing, distinct seeds)

- Initial release