BRAINS (BLR Reverberation-mapping Analysis In AGNs with Nested Sampling) dynamically models the broad-line regions of active galactic nuclei using reverberation-mapping and spectro-astrometric observations. It couples flexible geometric and kinematic BLR models with radiative transfer and line-response prescriptions to reproduce observed emission-line light curves, spectra, and spatial signatures. The code employs nested sampling to infer BLR structure and black hole mass, providing posterior distributions for physical and nuisance parameters and enabling rigorous model comparison. Implemented in C and Python, BRAINS supports configurable data sets, model components, and priors, and includes utilities for data preparation, parameter estimation, and visualization of inferred BLR and black hole properties.

PyCALI intercalibrates astronomical light curves using a Bayesian MCMC framework. It applies additive and multiplicative factors to light curves to bring them into a common scale by modeling the variability with a damped random walk process. Systematic error factors and error scale factors can also be incorporated.

BayeSED implements full Bayesian interpretation of spectral energy distributions (SEDs) of galaxies and AGNs. It performs Bayesian parameter estimation using posteriori probability distributions (PDFs) and Bayesian SED model comparison using Bayesian evidence. Its latest version BayeSED3 supports various built-in SED models and can emulate other SED models using machine learning techniques.