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ACKNOWLEDGMENT.md

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The ParaMonte library is an honor-ware and its currency is acknowledgment and citations.

If you use ParaMonte, please acknowledge it by citing the ParaMonte library's main publications as listed here:

The ParaMonte Python library

  • Amir Shahmoradi, Fatemeh Bagheri, Joshua Alexander Osborne (2020). Fast fully-reproducible streamlined serial/parallel Monte Carlo/MCMC simulations and visualizations via ParaMonte Python library.. Journal of Open Source Software (JOSS), to be submitted, PDF link.
    BibTeX citation entries: 
    
@article{2020arXiv201000724S,
       author = { {Shahmoradi}, Amir and {Bagheri}, Fatemeh and {Osborne}, Joshua Alexand
        er},
        title = "{Fast fully-reproducible serial/parallel Monte Carlo and MCMC simulations and visualizations via ParaMonte Python library}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Mathematical Software, Astrophysics - Instrumentation and Methods for Astrophysics, Quantitative Biology - Quantitative Methods, Statistics - Machine Learning},
         year = 2020,
        month = oct,
          eid = {arXiv:2010.00724},
        pages = {arXiv:2010.00724},
archivePrefix = {arXiv},
       eprint = {2010.00724},
 primaryClass = {cs.MS},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2020arXiv201000724S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

The ParaMonte C/C++/Fortran library

  • Amir Shahmoradi, Fatemeh Bagheri (2020). ParaMonte: A high-performance serial/parallel Monte Carlo simulation library for C, C++, Fortran. Journal of Open Source Software (JOSS), submitted, PDF link.
    BibTeX citation entries: 
    
@article{2020arXiv200914229S,
       author = { {Shahmoradi}, Amir and {Bagheri}, Fatemeh},
        title = "{ParaMonte: A high-performance serial/parallel Monte Carlo simulation library for C, C++, Fortran}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Mathematical Software, Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Data Analysis, Statistics and Probability, Quantitative Biology - Quantitative Methods, Statistics - Machine Learning},
         year = 2020,
        month = sep,
          eid = {arXiv:2009.14229},
        pages = {arXiv:2009.14229},
archivePrefix = {arXiv},
       eprint = {2009.14229},
 primaryClass = {cs.MS},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2020arXiv200914229S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

The ParaDRAM sampler

  • Amir Shahmoradi, Fatemeh Bagheri (2020). ParaDRAM: A Cross-Language Toolbox for Parallel High-Performance Delayed-Rejection Adaptive Metropolis Markov Chain Monte Carlo Simulations. Journal of Computer Methods in Applied Mechanics and Engineering (CMAME), submitted, PDF link.
    BibTeX citation entries: 
        
    @article{2020arXiv200809589S,
               author = { {Shahmoradi}, Amir and {Bagheri}, Fatemeh},
                title = "{ParaDRAM: A Cross-Language Toolbox for Parallel High-Performance Delayed-Rejection Adaptive Metropolis Markov Chain Monte Carlo Simulations}",
              journal = {arXiv e-prints},
             keywords = {Computer Science - Computational Engineering, Finance, and Science, Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Data Analysis, Statistics and Probability, Statistics - Computation, Statistics - Machine Learning},
                 year = 2020,
                month = aug,
                  eid = {arXiv:2008.09589},
                pages = {arXiv:2008.09589},
        archivePrefix = {arXiv},
               eprint = {2008.09589},
         primaryClass = {cs.CE},
               adsurl = {https://ui.adsabs.harvard.edu/abs/2020arXiv200809589S},
              adsnote = {Provided by the SAO/NASA Astrophysics Data System}
    }

The ParaMonte MatDRAM MATLAB library

  • Shashank Kumbhare, Amir Shahmoradi (2020). MatDRAM: A pure-MATLAB Delayed-Rejection Adaptive Metropolis-Hastings Markov Chain Monte Carlo Sampler. Journal of Computer Physics Communications (CPC), submitted, PDF link.
    BibTeX citation entries: 
        
    @article{2020arXiv201004190K,
               author = { {Kumbhare}, Shashank and {Shahmoradi}, Amir},
                title = "{MatDRAM: A pure-MATLAB Delayed-Rejection Adaptive Metropolis-Hastings Markov Chain Monte Carlo Sampler}",
              journal = {arXiv e-prints},
             keywords = {Physics - Data Analysis, Statistics and Probability, Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Computational Engineering, Finance, and Science, Quantitative Biology - Quantitative Methods, Statistics - Applications},
                 year = 2020,
                month = oct,
                  eid = {arXiv:2010.04190},
                pages = {arXiv:2010.04190},
        archivePrefix = {arXiv},
               eprint = {2010.04190},
         primaryClass = {physics.data-an},
               adsurl = {https://ui.adsabs.harvard.edu/abs/2020arXiv201004190K},
              adsnote = {Provided by the SAO/NASA Astrophysics Data System}
    }

For more information, visit the ParaMonte library homepage.