Fast automated cross-platform cross-language parallel stochastic optimization, sampling, and integration with the ParaMonte library.

ParaMonte (standing for Parallel Monte Carlo) is a serial as well as MPI/Coarray-parallelized library of (Markov Chain) Monte Carlo (MCMC) routines for sampling mathematical objective functions, the distributions of the posterior parameters of Bayesian models, and generic scientific inference problems in data science, Machine Learning. In addition to providing access to fast high-performance serial/parallel stochastic sampling routines, the ParaMonte library provides extensive post-processing and visualization tools that aim to automate and streamline the process of parameter estimation, uncertainty quantification, and model selection in Bayesian data analysis. Furthermore, the automatically-enabled restart functionality of ParaMonte samplers ensures a seamless fully-deterministic into-the-future restart of Monte Carlo simulations, should any interruptions happen. The ParaMonte library is MIT-licensed, cross-platform, and cross-language, currently available in C, C++, Fortran, MATLAB, Python, and R. The repository of the library is permanently maintained on GitHub at https://github.com/cdslaborg/paramonte.