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ACKNOWLEDGMENT.md

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

As per the ParaMonte library license agreement terms, if you use any parts of this library for any purposes, kindly acknowledge the use of ParaMonte in your work (education/research/industry/development/...) 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}
    }

For more information, visit the ParaMonte library homepage.