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Changelog

Source: NEWS.md

ospsuite.parameteridentification 2.0.1

Breaking changes

Minor improvements and fixes

ospsuite.parameteridentification 2.0.0

Breaking changes

  • Requires {ospsuite} version 12.0 or later (#98, #114, @rengelke).

  • PIConfiguration now configures the objective function via objectiveFunctionOptions. Users can specify options directly, including objectiveFunctionType, residualWeightingMethod, robustMethod, scaleVar, and linScaleCV (#100, @rengelke).

  • ParameterIdentification$gridSearch() andParameterIdentification$calculateOFVProfiles() functions have been disabled until fixed (#91, #92).

Major changes

  • New calculateCostMetrics() function in ParameterIdentification enhances model evaluation with integrated configuration via PIConfiguration. Settings and defaults are specified in ObjectiveFunctionOptions (#64, #65, #100, @rengelke).

    • objectiveFunctionType: lsq for least squares or m3 for censored data maximum likelihood estimation.
    • residualWeightingMethod: options include none, std normalizes by standard deviation for variable data, mean scales by mean for diverse magnitudes, and error weights by inverse variance for known error data.
    • robustMethod: none for uniform treatment, huber or bisquare for outlier minimization.
    • scaleVar: A boolean indicating whether to scale residuals by the number of observations.
    • linScaleCV: Numeric coefficient used to calculate standard deviation for linear scaling, applied to lloq values when m3method is used.
    • logScaleSD: Numeric standard deviation for logarithmic scaling, applied to lloq values when m3method is used.

  • New error-calculation vignette, explaining error model methodologies within the ospsuite.parameteridentification package. The document elaborates on the lsq (Least Squares Error) and m3 (Extended Least Squares Error for censored data) error models, along with advanced customization options for error modeling. This resource aids users in refining their parameter identification processes. (#102, #111, @rengelke).

  • New optimization-algorithms vignette, introducing algorithms available for parameter estimation and offering insights on their optimal application scenarios (#104, #111, @rengelke).

  • New user-guide vignette on parameter identification (PI). This vignette provides a comprehensive overview for setting up PI tasks, including defining simulations, specifying parameters to be identified, mapping model outputs to observed data, and configuring optimization tasks. Examples across three complexity levels of models demonstrate the package’s functionality in detail (#48, @svavil, @PavelBal).

  • New plot.modelCost() function for visualizing raw and weighted residuals from modelCost objects (#100, @rengelke).

  • Comprehensive overhaul of documentation, for clarity, comprehensiveness, and ease of navigation for all users (#111, @rengelke).

  • Unit test overhaul and coverage enhancement, increasing from 67% to 85% (#99, #100, #113, @rengelke).

Minor improvements and fixes

  • Enhanced error handling now ensures ParameterIdentification tasks validate the existence of simulation objects referenced by PIParameters and OutputMapping to prevent runtime errors due to missing dependencies (#117, #120, @rengelke).

  • Enhanced calculateCostMetrics() output in ParameterIdentification offers a more detailed results summary for model evaluation. The summary now includes modelCost, minLogProbability, and a costVariables dataframe with scaleFactor, nObservations, M3Contribution, SSR (sum of squared residuals), weightedSSR, normalizedSSR, and robustSSR (#100, @rengelke).

  • README file conversion to .rmd format and subsequent updates (#86, #93, @FelixMil).

  • Improved error handling in ParameterIdentification for cases of simulation failure, ensuring consistent and informative error cost structure in output (#66, #70, #100, @svavil, @PaveBal, @rengelke).

  • validateIsOption() ensures user-specified options adhere to defined constraints, enhancing the robustness of user inputs (#100, @rengelke).

  • ParameterIdentification now directly accesses default optimization algorithm options for BOBYQA, HJKB, and DEoptim from their respective packages (nloptr, {dfoptim}, and DEoptim) if not explicitly defined in PIConfiguration (#48, #81, @PavelBal).

  • Continuous Integration/Continuous Deployment pipeline improvements (#95, #106, #110, @FelixMil)

  • Several bug fixes (#83, #109, #110, #115, #119, #122, @PavelBal, @FelixMil, @rengelke)

ospsuite.parameteridentification 1.3

Breaking changes

  • The parameter in the PIConfiguration class that is controlling the feedback at each function evaluation is now called printEvaluationFeedback instead of printItera tionFeedback.

Major changes

  • Added new optimization algorithms: the default local algorithm is now an implementation of the BOBYQA algorithm (bounded optimization by quadratic approximation) from the nloptr package; additional local algorithm is HJKB, a bounded implementation of the Hooke-Jeeves derivative-free algorithm from the {dfoptim} package; a global algorithm is DEoptim for differential evolution optimization.

  • FME::modCost() is re-implemented as part of the parameter identification package and used for calculation of residuals.

Minor bug fixes and improvements

  • Calculation of residuals does not fail if observed data contains only one time point.
  • Calculation of the hessian close to the bounds of parameter values is improved.

ospsuite.parameteridentification 1.2

Breaking changes

Minor bug fixes and improvements

  • getSteadyState() accepts steady state time individually for each simulation.