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Original author(s) | The Computational Data Science Lab |
---|---|
Initial release | June 1, 2012 |
Stable release | 1.5.1
/ Jan 1, 2021 |
Written in | C, C++, Fortran, MATLAB, Python |
Operating system | Unix-like, Microsoft Windows, Mac OS X |
Platform | Intel x86 - 32-bit, x64, Aarch64 |
Type | Statistical package |
License | MIT license |
Website | cdslab |
ParaMonte[1] (standing for Parallel Monte Carlo) is an open-source, permissively-licensed (e.g., MIT License), serial and MPI / OpenMP / Coarray-parallelized library of Monte Carlo and Machine Learning algorithms for mathematical optimization, statistical sampling (e.g., Markov Chain Monte Carlo (MCMC)), and deterministic and stochastic numerical integration of mathematical density functions of arbitrary dimensions, in particular, the posterior distributions of Bayesian models in data science, machine learning, and scientific inference[2]. The library is written in a mixture of programming languages, primarily consisting of modern Fortran, C, C++, MATLAB, Python[3].
The ParaMonte Fortran library contains nearly 1 million lines[4] of fully generic multi-precision interfaces and routines for various Monte Carlo integration and machine learning tasks (e.g., cluster analysis[5]) written in Fortran-2008. The Fortran version of the library also contains fully modernized, generic, multi-precision implementations and significant extensions of the FFTPACK[6] for Fast Fourier Transform[7] and QUADPACK[8] for integration using Adaptive Quadrature methods[9].
The MATLAB[10] and Python[11] versions of the library are available through MathWorks MATLAB Central FileExchange[12] and Python Package Index[13], respectively. The library is released with many examples[14] and usage instructions[15] for different programming languages and is fully documented for all supported programming languages (e.g., C[16], C++[17], Fortran[18], MATLAB[19]).
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