In the following, all demos of Monolix are presented. They were built to explore all functionalities of Monolix in terms of model creations, continuous and non continuous outcomes management, joint models for multivariate outcomes, models for the individual parameters, pharmacokinetic models, and some extensions.

#### Defining a data set

- Defining a data set: learn how to define a data set for Monolix.

#### Creating and using models

- Libraries of models: learn how to use the Monolix libraries of PKPD models and create your own libraries.
- Use your own model: learn how to use your own libraries created from scratch or from the libraries.
- Outputs and Tables: learn how to define outputs and create tables with selected outputs of the model.

#### Models for continuous outcomes

- Residual error model: learn how to use the predefined residual error models.
- Handling censored data: learn how to handle easily and properly censored data, i.e. data below (resp. above) a lower (resp.upper) limit of quantification (LOQ) or detection (LOD).
- Mixture of structural models: learn how to implement between subject mixture models (BSMM) and within subject mixture models (WSMM).

#### Models for non continuous outcomes

- Time-to-event data model: learn how to implement a model for (repeated) time-to-event data.
- Count data model: learn how to implement a model for count data, including hidden Markov model.
- Categorical data model: learn how to implement a model for categorical data, assuming either independence or a Markovian dependence between observations.

#### Joint models for multivariate outcomes

- Continuous PKPD model: learn how to implement a joint model for continuous pharmacokinetics-pharmacodynamics (PKPD) data.
- Joint continuous and non continuous data model: learn how to implement a joint model for continuous and non continuous data, including count, categorical and time-to-event data.

#### Models for the individual parameters

- Introduction
- Probability distribution of the individual parameters: learn how to define the probability distribution and the correlation structure of the individual parameters.
- Model for individual covariates: learn how to implement a model for continuous and/or categorical covariates.
- Inter occasion variability: learn how to take into account inter occasion variability (IOV).
- Mixture of distributions: learn how to implement a mixture of distributions for the individual parameters.

#### Pharmacokinetic models

- Single route of administration: learn how to define and use a PK model for single route of administration.
- Multiple routes of administration: learn how to define and use a PK model for multiple routes of administration.
- From multiple doses to steady-state: learn how to define and use a PK model with multiple doses or assuming steady-state.

#### Some extensions

- Using regression variables: learn how to define and use regression variables (time varying covariates).
- Bayesian estimation: learn how to combine maximum likelihood estimation and Bayesian estimation of the population parameters.
- Delayed differential equations : learn how to implement a model with delayed differential equations (DDE).