Releases | Open Source Science

pymcmcstat - v1.9.1

Fixed #79 - Chainstats display now auto updates formatting for really large or small numbers.
Fixed #82 - Initial value check routine in Parallel MCMC now correctly removes extra columns.

Scientific Software - Peer-reviewed - Jupyter Notebook
Published by prmiles over 5 years ago

pymcmcstat - v1.9.0

Added feature to results structure to store all parameters names, regardless of whether or not they are included in sampling.
Added mcmcplot package to requirements. MCMCPlotting module is noted as deprecated.
Added new module for uncertainty propagation. Aims to provide more flexible API for user to plot different combinations of credible and prediction intervals.
Added a plotting routine so that you can plot a 2-D interval in 3-D space.

Scientific Software - Peer-reviewed - Jupyter Notebook
Published by prmiles almost 7 years ago

pymcmcstat - v1.8.0

Added acceptance rate display feature when calling chain statistics
User can specify skip or maxpoints in pairwise correlation and chain panel plots in order to thin chain.
User can request item definitions when calling chainstats.
Resolved #51 by adding method to check if limits are incompatible. User is provided with more descriptive error message.
Updated documentation for Bayesian components.
Added saving routines to accomodate post-processing. A lighter version of the results dictionary is saved to json when log routines are used for chains. This should address #55.
Option to return KDE objects when using plot_density_panel. User can then evaluate pdf directly. This should address #66.

Scientific Software - Peer-reviewed - Jupyter Notebook
Published by prmiles almost 7 years ago

pymcmcstat - v1.7.1

Fixed bug #41.
Updated how version attribute is defined in package and referenced in setup.py and documentation.

Scientific Software - Peer-reviewed - Jupyter Notebook
Published by prmiles about 7 years ago

pymcmcstat - v1.7.0

Added custom sampler feature. Interface requires creation of a class, but allows for inclusion of things like Gibbs samplers. User can explicitly pass in features to the sum-of-squares function.
Added check routines for reading in parallel MCMC results to ensure only directories with name 'chain_' are queried.
All files were updated to comply with formatting standards using flake8.
Added coverage report check to ensure minimal testing requirements are met.
Added original covariance matrix to results structure for reference.

Scientific Software - Peer-reviewed - Jupyter Notebook
Published by prmiles about 7 years ago

pymcmcstat - v1.7.0rc2

Updated docs

Scientific Software - Peer-reviewed - Jupyter Notebook
Published by prmiles about 7 years ago

pymcmcstat - v1.7.0rc1

Test release for v1.7.0

Scientific Software - Peer-reviewed - Jupyter Notebook
Published by prmiles about 7 years ago

pymcmcstat - v1.6.0

Final release of v1.6.0.

Added optimal handling of prior function used numpy array multiplication.
Fixed bug with saving results to json file - output directory now automatically made.
Updated PI plotting to allow any valid matplotlib input.
Added Gelman-Rubin diagnostics to ChainStatistics module, with display feature.
Added numpy error settings option to MCMC initialization.
Setup no adaptation feature. Can sample parameters without adapting them.
Added restart routine to ParallelMCMC. Can restart from json files.
Added parallel chain organization routines to the chain subpackage.

Scientific Software - Peer-reviewed - Jupyter Notebook
Published by prmiles almost 8 years ago

pymcmcstat - v1.6.0rc1

Initial test release of v1.6.0.

Added optimal handling of prior function used numpy array multiplication.
Fixed bug with saving results to json file - output directory now automatically made.
Updated PI plotting to allow any valid matplotlib input.
Added Gelman-Rubin diagnostics to ChainStatistics module, with display feature.
Added numpy error settings option to MCMC initialization.
Setup no adaptation feature. Can sample parameters without adapting them.
Added restart routine to ParallelMCMC. Can restart from json files.
Added parallel chain organization routines to the chain subpackage.

Scientific Software - Peer-reviewed - Jupyter Notebook
Published by prmiles almost 8 years ago

pymcmcstat - v1.5.0

Final release version of 1.5.0

Scientific Software - Peer-reviewed - Jupyter Notebook
Published by prmiles almost 8 years ago

pymcmcstat - v1.5.0rc2

Added Zenodo link to release

Scientific Software - Peer-reviewed - Jupyter Notebook
Published by prmiles almost 8 years ago

pymcmcstat - v1.5.0rc1

Release candidate 1 of v1.5.0

Updated analysis options and chain diagnostics
Improved workflow on remote operating systems

Scientific Software - Peer-reviewed - Jupyter Notebook
Published by prmiles almost 8 years ago

pymcmcstat - v1.4.0

This release has the following notable updates:

complete unit test coverage
documentation on all main methods/classes
wrapper routine for running chains in parallel
advanced control for plotting credible/prediction intervals
log files saved to binary format

Scientific Software - Peer-reviewed - Jupyter Notebook
Published by prmiles almost 8 years ago

pymcmcstat - v1.3.1

Updated a few minor - plotting: automatic name generation and filling (if none/insufficient given) - bin save: added covariance matrix to feature - debugged sample feature

Scientific Software - Peer-reviewed - Jupyter Notebook
Published by prmiles about 8 years ago

Primary Feature(s): - This releases of pymcmcstat has been updated for compatibility with Python 3.6 (future releases will be developed in Python 3.6). - The updates are still compatible with Python 2.7.

Scientific Software - Peer-reviewed - Jupyter Notebook
Published by prmiles about 8 years ago

pymcmcstat - v1.2.0

Primary feature(s): - Ellipse contour routine for pairwise plots - Progress bar to prediction/credible interval generation - Bin save function: user can periodically save chain, sschain, and s2chain to a file.

Scientific Software - Peer-reviewed - Jupyter Notebook
Published by prmiles about 8 years ago

pymcmcstat - v1.1.1

Standard release - fixed bug associated with using PI routines for generating credible intervals without prediction intervals