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>.

Datasets for 'nlmixr2' and 'rxode2'. 'nlmixr2' is used for fitting and comparing
    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>).

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>).

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>). This package is for
    support functions like preconditioned fits
    <doi:10.1208/s12248-016-9866-5>, boostrap and stepwise covariate
    selection.