pyABC 0.10.4

• Refactor __all__ imports and docs API build (#312).
• Fix json export of aggregated adaptive distances (#316).
• Apply additional flake8 checks on code quality (#317).
• Assert model input is of type pyabc.Parameter (#318).
• Extend noise notebook to estimated noise parameters (#319).
• Implement optional pickling for multicore samplers; add MacOS
  pipeline tests (#320).

pyABC 0.10.3

• Speed up multivariate normal multiple sampling (#299).
• Set default value for OMP_NUM_THREADS=1, stops warnings (#299).
• Base default number of parallel cores on PYABC_NUM_PROCS (#309).
• Update all notebooks to the latest numpy/scipy (#310).

pyABC 0.10.2

• Update CI test system: latest Ubuntu, python 3.8, simplify R build (#296).
• Add weights logging to adaptive distances (#295).
• Migrate CI tests to GitHub Actions for speed-up, reliability and
  maintainability (#297, #298).

pyABC 0.10.1

• Allow separate calibration population sizes, slightly reformulate PopulationStrategy class (#278).
• Allow specifying initial weights for adaptive distances, then without sampling from the prior (#279).
• Check PEtab test suite in tests (#281).

pyABC 0.10.0

• Exact inference via stochastic acceptor finalized and tested (developed
  throughout the 0.9 series).
• Support basic PEtab functionality using AMICI ODE simulations (#268).
• Various error fixes (#265, #267).
• Log number of processes used by multiprocessing samplers (#263).
• Implement pyabc.acceptor.ScaledPDFNorm (#269).
• Implement list population size (#274, #276).
• On history loading, automatically find an id of a successful run (#273).

pyABC 0.9.26

• Add optional check whether database is non-existent, to detect typos.
• Set lower bound in 1-dim KDEs to <= 0 to not wrongly display near-uniform
  distributions. (both #257)
• Implement redis password protection for sampler and manage routine (#256).
• Make samplers available in global namespace (#249).
• Implement ListTemperature (#248).
• Allow plotting the relative ESS (#245).
• Allow resampling of weighted particles (#244).
• Fix ABCSMC.load with rpy2 (#242).

pyABC 0.9.25

• Add summary statistics callback plot function (#231).
• Add possibility to log employed norms in StochasticAcceptor (#231) and
  temperature proposals in Temperature (#232).
• Implement optional early stopping in the MulticoreEvalParallelSampler and
  the SingleCoreSampler, when a maximum simulation number is exceeded
  (default behavior untouched).
• Log stopping reason in ABCSMC.run (all #236).
• Implement Poisson (#237) and negative binomial (#239) stochastic kernels.
• Enable password protection for Redis sampler (#238).
• Fix scipy deprecations (#234, #241).

pyABC 0.9.24

• In ABCSMC.run, allow a default infinite number of iterations, and log the
  ESS in each iteration.
• Reformulate exponential temperature decay, allowing for a fixed number of
  iterations or fixed ratios.
• Solve acceptance rate temperature match in log space for numeric stability.
• Perform temperation of likelihood ratio in log space for numeric stability
  (all #221).
• Fix wrong maximum density value in binomial kernel.
• Allow not fixing the final temperature to 1 (all #223).
• Allow passing id to history directly (#225).
• Pass additional arguments to Acceptor.update.
• Give optional min_rate argument to AcceptanceRateScheme (all #226).
• In plot functions, add parameter specifying the reference value color (#227).

pyABC 0.9.23

• Fix extras_require directive.
• Fix error with histogram plot arguments.
• Extend test coverage for visualization (all #215).
• ABCSMC.{new,load,run} all return the history with set id for convenience.
• Document pickling paradigm of ABCSMC class (see doc/sampler.rst).
• Always use lazy evaluation in updates (all #216).
• Restructure run function of ABCSMC class (#216, #218).
• Run notebooks on travis only on pull requests (#217).
• Correct weighting in AcceptanceRateScheme (#219).

pyABC 0.9.22

• Fix error that prevented using rpy2 based summary statistics with non rpy2 based models (#213).