Estimation of relatively complex nonlinear mixed-effects models, including the Sigmoidal Mixed Model and the Piecewise Linear Mixed Model with abrupt or smooth transition, through a single intuitive line of code and with automated generation of starting values.

Fit and compare Gaussian linear and nonlinear mixed-effects models.

Provides functions and datasets to support the book
   by Galecki and Burzykowski (2013),
   'Linear Mixed-Effects Models: A Step-by-Step Approach',
   Springer. Includes functions for power calculations,
   log-likelihood contributions, and data simulation
   for linear mixed-effects models.

Various visual and numerical diagnosis methods for the nonlinear mixed effect model, including visual predictive checks, numerical predictive checks, and coverage plots (Karlsson and Holford, 2008, <https://www.page-meeting.org/?abstract=1434>).

Fit and compare nonlinear mixed-effects models in differential
    equations with flexible dosing information commonly seen in pharmacokinetics
    and pharmacodynamics (Almquist, Leander, and Jirstrand 2015
    <doi:10.1007/s10928-015-9409-1>). Differential equation solving is
    by compiled C code provided in the 'rxode2' package
    (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).

Automated population pharmacokinetic modeling framework for
    data-driven initialisation, model evaluation, and metaheuristic
    optimization. Supports genetic algorithms, ant colony optimization,
    tabu search, and stepwise procedures for automated model selection
    and parameter estimation within the nlmixr2 ecosystem.

Provides automated methods for generating initial parameter estimates 
    in population pharmacokinetic modeling. The pipeline integrates adaptive single-point 
    methods, naive pooled graphic approaches, noncompartmental analysis methods, and parameter 
    sweeping across pharmacokinetic models. It estimates residual unexplained variability 
    using either data-driven or fixed-fraction approaches and assigns pragmatic initial values 
    for inter-individual variability. These strategies are designed to improve model 
    robustness and convergence in 'nlmixr2' workflows. For more details see 
    Huang Z, Fidler M, Lan M, Cheng IL, Kloprogge F, Standing JF (2025) 
    <doi:10.1007/s10928-025-10000-z>.