neuralsbi: Neural Simulation-Based Inference

A native R implementation of neural simulation-based inference, focused on Neural Posterior Estimation. Given a prior over parameters and a simulator, 'neuralsbi' trains a conditional neural density estimator to approximate the Bayesian posterior, enabling amortized, likelihood-free inference. Neural estimators run on the 'torch' back end. It targets applied researchers who want an approachable interface with sensible defaults and built-in posterior diagnostics.

See also

Useful links:

• https://neuralsbi.pedrodelima.com/

• https://github.com/pedroliman/neuralsbi

• Report bugs at https://github.com/pedroliman/neuralsbi/issues

Author

Maintainer: Pedro Nascimento de Lima plima@rand.org (ORCID)

Authors:

• Pedro Nascimento de Lima plima@rand.org (ORCID)