Pyro is a deep probabilistic programming library built on PyTorch, providing a unified framework for Bayesian inference, variational inference, and Monte Carlo methods. Its architecture centers on composable effect handlers (poutine) that enable tracing, conditioning, replay, and reparameterization of probabilistic programs. The library includes a rich set of distributions, bijective transforms for normalizing flows, automatic guide construction, and a variety of inference algorithms (SVI, HMC, NUTS, SVGD, SMC). It also offers domain-specific contrib modules for Gaussian processes, time series forecasting, epidemiology, optimal experimental design, and multi-object tracking. While primarily a software library, Pyro has strong research roots, with many features inspired by recent papers and experimental modules that may evolve. The codebase is well-structured, extensively tested, and actively maintained, though some contrib modules carry instability warnings and a few internal patches to PyTorch may require attention.
NIMBLE is an R package for hierarchical Bayesian modeling that implements a domain-specific language (BUGS) for model specification and compiles models to C++ for efficient MCMC, Laplace approximation, and other inference algorithms. The architecture is a hybrid R/C++ system where R handles model parsing, compilation orchestration, and high-level algorithm construction, while generated C++ code (using embedded Eigen and CppAD libraries) performs efficient sampling and automatic differentiation. Key entry points include `nimbleModel()`, `buildMCMC()`, `runMCMC()`, and `compileNimble()`. The package provides a 'programming with models' framework allowing users to write custom inference algorithms that operate on arbitrary model structures. It bundles extensive classic BUGS benchmarks for regression testing. While the core functionality is well-maintained, the vendored Eigen and CppAD libraries and the complex compilation system introduce potential drift risks.