Releases: jmrohwer/identifiability
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 usingmultiprocessing
, additional dependencies are required; these can be installed withpip 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