Submission declined on 5 July 2024 by
SafariScribe (
talk). This submission is not adequately supported by
reliable sources. Reliable sources are required so that information can be
verified. If you need help with referencing, please see
Referencing for beginners and
Citing sources. This draft's references do not show that the subject
qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are:
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
| ![]() |
![]() | |
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]).
Category:Computational statistics Category:Free Bayesian statistics software Category:Monte Carlo software Category:Numerical programming languages Category:Domain-specific programming languages Category:Probabilistic software