From there the token text in the template can be dragged and dropped to the appropriate place in the template file text (figure 2, blue ellipse). The control file resulting from the currently selected token sets can be generated for examination by clicking the “View Current Control stream” button (figure 2, red ellipse).
A complete set of default options can be selected for each of the available algorithms in the “options” tab. These options can then be edited if needed. The analysis can then be run, the progress and intermediate output monitored and diagnostic plots generated (figure 3) from the DarwinReporter R Shiny application.
Use scenarios for pyDarwin would include large machine learning model selection, or very simple searches. For example, a search of 1 vs 2 compartment models with 4 different sets of initial estimates (2 each for V and CL) could be done rather than manually coding and executing all 8 models, making pyDarwinDesigner a general and efficient tool for most model development problems.
Li, X.,et.al. (2024), pyDarwin: A Machine Learning Enhanced Automated Nonlinear Mixed-Effect Model Selection Toolbox. Clin Pharmacol Ther. https://doi.org/10.1002/cpt.3114