ParaMonte MATLAB release notes

This project follows Semantic Versioning. To access the latest release of the package, visit the ParaMonte GitHub repository release page.

Version 2.x.x

Version 2.2.1 – November 15, 2020

Minor enhancements

  • Minor enhancements to the Kernel library build scripts and dependencies management.

  • More informative error messages are now printed on MATLAB console if any error happens during the ParaMonte library setup on macOS for the first time.

Version 2.2.0 – October 29, 2020


  • The cmake software dependency installation failure now does not nullify the installation of other dependencies.

  • The IO debugging info of all ParaMonte samplers have been enhanced. In cases of wrong syntax or syntax-breaking input values in the simulation output files, the error messages are now more informative and point directly to the exact location of of error in the input file.

  • The Integrated Autocorrelation (IAC) for sample refinement in ParaDRAM sampler of ParaMonte is now set to the average of all variables’ IAC values instead of the maximum IAC value. This will lead to less aggressive decorrelation of the final sample, which means significantly larger final sample sizes, without compromising the i.i.d. property of the final refined sample. This behavior can be reversed back to the original by specifying “max” or “maximum” along with the requested refinement method, SampleRefinementMethod = "batchmeans max" or SampleRefinementMethod = "BatchMeans-max" (case-insensitive).

Version 2.1.3 – October 15, 2020

Minor enhancements

  • Further minor enhancements to the behavior of the checkForUpdate() method of the paramonte class.

Version 2.1.2 – October 15, 2020

Minor enhancements

  • The checkForUpdate() method of the paramonte class now functions as expected.

Version 2.1.1 – October 9, 2020

Minor enhancements

  • A Linux bug in the installation of the MPI library is now fixed.

Version 2.1.0 – October 3, 2020

Minor enhancements

  • A new simulation specification overwriteRequested has been added to all ParaMonte samplers. If True and the ParaMonte sampler detects an existing set of old simulation output files in the output path of the current simulation with the same names as the output file names of the current simulation, then, the ParaMonte sampler will overwrite the existing simulation files.

Version 2.0.1 – September 26, 2020

Minor enhancements

  • The guidelines for the installation of the MPI library on macOS have been improved.

  • The minor bug in GridPlot class method rotateAxesLabels() that caused the readSample() , readChain(), readMarkovChain() to crash upon adding Grid plots is now fixed.

  • The minor bug in the naming of the ParaMonte kernel library files on macOS (Darwin) is now fixed.

Version 2.0.0 – September 22, 2020

Major enhancements to the ParaMonte / ParaDRAM sampler interfaces

  • The entire ParaMonte MATLAB interface library has been revamped. The new naming conventions, visualization, and computing tools are significantly nicer to deal with and in some cases, orders of magnitude faster than the previous major release.

  • The simulation output files reading is now completely overhauled. In particular, the output file reader methods are now capable of handling input file paths that point to a directory. In such cases, it will search the input directory for files matching the requested file name pattern. If no input file is provided to the file reader methods, the current working directory will be search for the the potential simulation files that match the requested pattern.

  • Several new post-processing functionalities have now been added, such as the ability to seamlessly parse the contents of the output *_report.txt, *_restart.txt, and *_progress.txt simulation files, in addition to the other output files (*_sample.txt and *_chain.txt) that could be parsed in the previous versions.

  • The newly-added readRestart() method is now added to the ParaDRAM sampler class. User can now parse the contents of the output ASCII-format restart files. This is particularly useful to visualize the dynamics of the ParaDRAM sampler class, such as the evolution of the proposal distribution’s location, shape, and covariance matrix.

  • The GridPlot() class now has two additional methods setAxesLabels() and setAxesLimits() which can directly set the labels and limits of axes, hassle-free.

Minor enhancements

  • The single value assignment to spec.targetAcceptanceRate component of a ParaDRAM object is now properly handles. For example, the following code is valid as expected,
    import paramonte as pm
    pmpd = pm.ParaDRAM()
    pmpd.spec.targetAcceptanceRate = 0.23 # this is now valid
    pmpd.spec.targetAcceptanceRate = [0.2, 0.3] # this is also valid, which limits the acceptance rate to the specified range
  • The default background color in all plots is now "white".
  • The rotateAxisLabels() of the GridPlot() class is now renamed to rotateAxesLabels().

Bug fixes

  • ParaDRAM readMarkovChain() no-output-option bug is now fixed. When calling readMarkovChain(), user can now either provide the output variable or not.

Version 1.x.x

Version 1.1.0 – June 5, 2020

  • Enhancements and bug fixes to the kernel routines.
  • Several major enhancements and bug fixes to the MATLAB kernel and interface routines.
  • MatDRAM now supports fully-deterministic restart functionality.

Version 1.0.0 – June 1, 2020 – Initial release

  • This is the first public release of the ParaMonte MATLAB library.

New features

  • ParaDRAM sampler: Parallel Delayed-Rejection Adaptive Metropolis-Hastings Markov Chain Monte Carlo Sampler.
  • ParaMonte Interface to the MATLAB Programming language.
  • ParaMonte simulation-output visualization via the ParaMonte MATLAB interface.