identifiability - release 0.4

New point release.

What's new?

• Use PySCeS for parameter identifiability analysis of kinetic models using the CVODE solver. When performing identifiability analysis in parallel using multiprocessing, additional dependencies are required; these can be installed with pip install "identifiability[pyscesmp]".
• Make the degree of the spline used for the profile likelihood plot configurable (default is 2).
• The params dictionary is added to the trace dictionary for every step to track all parameters.
• The complete optimization result object is now available from the trace dictionary - useful for troubleshooting individual parameter runs.
• Standard error estimates are no longer required in the input optimization result, as they are not needed for this method.

© Johann M. Rohwer, October 2023

identifiability - release 0.3.2

Minor bugfix release.

What's new?

• Fix bugs in multiplot plotting code for CIs.

© Johann M. Rohwer, April 2022

identifiability - release 0.3.1

Minor release with some updates.

What's new?

• Implement recalculation of confidence intervals with a different probability, without re-calculating the whole profile likelihood (which is computationally expensive).
• Add Github continuous integration for automatic wheel builds and PyPI upload.

© Johann M. Rohwer, April 2022

identifiability - release 0.3

What's new?

• Optimization can now be performed in parallel using the multiprocessing module.
• Added a method ConfidenceInterval.plot_all_ci() to plot confidence intervals for all the parameters analysed.
• First release on PyPI, the module can now be installed simply with pip install identifiability.

© Johann M. Rohwer, April 2022

identifiability - release 0.2

This module performs parameter identifiability analysis to calculate and plot confidence intervals based on a profile-likelihood. The code is adapted from LMFIT, with custom functions to select the range for parameter scanning and for plotting the profile likelihood. The significance is assessed with the chi-squared distribution.

What's new?

• add support for using the LMFIT Model class
• make handling of limits more consistent between linear and log parameter scans
• various bug fixes

© Johann M. Rohwer, February 2022

identifiability - release 0.1

First public release.

This module performs parameter identifiability analysis to calculate and plot confidence intervals based on a profile-likelihood. The code is adapted from LMFIT, with custom functions to select the range for parameter scanning and for plotting the profile likelihood. The significance is assessed with the chi-squared distribution.

© Johann M. Rohwer, December 2021