ABSTRACT
The tools presented in this appendix all share the Mlxtran language developed by Lixoft and Inria to implement mixed effects models.
Monolix, Mlxplore and Datxplore are currently developed by Lixoft and are available at https://lixoft.com
Simulx was developed by Inria1 and is available at https://team.inria.fr/popix/mlxtoolbox
The projects, models and data files used in this chapter are available at https://www.math.u-psud.fr/~lavielle/book
D.1 Mlxplore for model exploration Mlxplore allows us to visualize not only the structural model but also the statistical model, which is of fundamental importance in the population approach. We can thus visualize the impact of covariates and inter-individual variability of model parameters on predictions. In the modeling context, we may also want to visually calibrate parameters in order to obtain predictions as close as possible to the observations.
