ParaMonte - A cross-platform parallel scalable high-performance Monte Carlo optimization, sampling, and integration library in C, C++, Fortran, MATLAB, Python, and R

Predictive Science involves observational data collection, developing testable hypotheses, & making predictions. The scientific theory developed can be cast into a mathematical objective function that goes from various steps of model calibration, validation, & prediction of the Quantity of interest.
For decades, the Markov Chain Monte Carlo algorithms, especially the Metropolis-Hastings algorithm, are widely used for stochastic optimization, sampling, & integration of mathematical objective functions, in the context of Machine Learning, Bayesian inverse problems, & parameter estimation. Here, we present the ParaMonte software, a suite of parallel Monte Carlo optimization, sampling, & integration algorithms for Bayesian inference problems.
The primary goal of the ParaMonte library is to streamline scientific inference by full automation & by providing runtime dynamic directions to the user. It also offers fully-deterministic reproducible restart functionality of all simulations & a unified API accessible from several major scientific & Data Science programming languages, including C, C++, Fortran, MATLAB, Python, & R. Comprehensive automated post-processing tools integrated with the ParaMonte library also enable seamless analysis & visualization of the simulation results.