List of known bugs

The list of known bugs in every version of MonolixSuite can be found in the release notes of the next version.

We list below the known bugs in MonolixSuite 2024R1 (they will be fixed in the next version). The 2024R1 version of Monolix can be downloaded here.

When changing the structural model for a model with additional observation ids, the mapping for the additional observation ids is not done automatically.
[Bootstrap] After running bootstrap on a project where there are effects of several covariates on the same parameter, with at least one of the covariates that is transformed, the beta values for covariate effects are swapped in the table of estimated population parameters in Estimation compared to the bootstrap results.
[TGI library] The TGI model named TG_Exp_NoFeat_TS0par_TGI_t0Trt_LK_Const_Claret_SD_NoFeat includes a parameter tau that is not used in the model, while it should be used to define a delay.
Typical values of population parameters by categories of categorical covariates are not displayed for latent covariates.
copyData is an option available in config files to run model building, but not for convergence assessment even though the option is available in the GUI.

Clicking on Check lambda_z tab triggers a star (unsaved changes) even though the project has not changed.
Exporting to Simulx a PKanalix project where a covariate is used in a custom NCA parameter, without that covariate, results in an error.
Ctau and AUC_TAU are not correctly calculated when there is no observation points at the end of the interval (time=tau) and lambda_z could not be calculated. Instead of being NaN, Ctau is equal to Clast and AUC_TAU is equal to AUC_last. This bug is also present in previous versions. Note that when lambda_z is available, Ctau and AUC_tau are correctly calculated bu extrapolating the concentration until time=tau using lambda_z.

getOutputElements() returns data truncated to 5000 lines, similarly to what is done in the GUI to speed up the display.
A VPC split by some groups generated with plotVpc() can show issues in the prediction intervals if the max binLimit is different across groups. The workaround is to use filter instead of split to generate the VPC separately for each group.