The statistical software package pySELFI implements the Simulator Expansion for Likelihood-Free Inference (SELFI) algorithm. SELFI is part of the family of simulation-based inference (SBI) methods, which replace the use of the likelihood function with a data-generating "black-box" simulator. pySELFI supports different black-box simulators, optimizes prior hyperparameters for primordial power spectrum inference, and check for model misspecification using the Mahalanobis distance. The code also scores compression and calculation of Fisher-Rao distances.

Simbelmynë generates synthetic galaxy survey data using a hierarchical probabilistic simulator. It is a parallel code and thus can generate a large number of realizations of synthetic galaxy survey data. The package can simulate the physics of dark matter structure formation via non-linear models, survey geometry, manage galaxy bias and selection effects, and contains modules containing cosmic web classifiers and phase-space field estimators. Intended for use within cosmological analyses of real survey data, Simbelmynë has been developed as a self-contained, end-to-end generative process, with a focus on speed and accuracy at the relevant scales.