  The Python package pyABC provides a framework for approximate Bayesian
computation (ABC), a likelihood-free parameter inference method popular in many
research areas. At its core, it implements a sequential Monte-Carlo (SMC)
scheme, with various algorithms to adapt to the problem structure and
automatically tune hyperparameters. To scale to computationally expensive
problems, it provides efficient parallelization strategies for multi-core and
distributed systems. The package is highly modular and designed to be easily
usable. In this major update to pyABC, we implement several advanced algorithms
that facilitate efficient and robust inference on a wide range of data and
model types. In particular, we implement algorithms to account for noise, to
adaptively scale-normalize distance metrics, to robustly handle data outliers,
to elucidate informative data points via regression models, to circumvent
summary statistics via optimal transport based distances, and to avoid local
optima in acceptance threshold sequences by predicting acceptance rate curves.
Further, we provide, besides previously existing support of Python and R,
interfaces in particular to the Julia language, the COPASI simulator, and the
PEtab standard.
