Automatic Generation of Initial Estimates for Population Pharmacokinetic Modeling
Linear-up and log-down trapezoidal rule
Linear trapezoidal rule
Computes the trimmed geometric mean
Print method for getPPKinits objects
Process time–concentration dataset for pharmacokinetic analysis
Run graphical analysis of pharmacokinetic parameters
Run adaptive single-point pharmacokinetic analysis
Perform extended single-point pharmacokinetic calculations
Generate ETA variance and covariance table
Approximate volume of distribution from observed Cmax
Bin time-concentration data using quantile or algorithmic binning
Calculate clearance using an adaptive single-point method
Calculate time after dose for pharmacokinetic data
Automated pipeline for generating initial estimates in population PK m...
Calculates volume of distribution from concentration data
Evaluates predictive performance of a one-compartment model
Control settings for fallback rules in parameter estimation
Find the best terminal elimination rate constant (lambdaz)
Fit intravenous pharmacokinetic data to a one-compartment linear elimi...
Fit intravenous pharmacokinetic data to a one-compartment model with M...
Fit oral pharmacokinetic data to a one-compartment model with Michaeli...
Fit oral pharmacokinetic data to a one-compartment linear elimination ...
Fit intravenous pharmacokinetic data to a two-compartment linear elimi...
Fit oral pharmacokinetic data to a two-compartment model
Fit intravenous pharmacokinetic data to a three-compartment linear eli...
Fit oral pharmacokinetic data to a three-compartment linear eliminatio...
Forceful estimation of terminal slope
Estimate half-life from pooled pharmacokinetic data
Generate pooled data for pharmacokinetic analysis
Perform non-compartmental pharmacokinetic analysis
Compute overall residual variability from elimination phase
Estimate individual-level residual error from the elimination phase
Graphical calculation of clearance and volume of distribution (IV rout...
Graphical calculation of pharmacokinetic parameters for oral administr...
Estimate absorption rate constant in a one-compartment oral model
Generate Unique Mixture Parameter Grid (with Deduplication and NA Remo...
Create full control list for initial parameter estimation
Determine steady state for pharmacokinetic observations
Calculate absorption rate constant (ka) in a multiple-dose one-compart...
Control settings for pooled data analysis
Calculate the absorption rate constant using the Wagner-Nelson method
Mark dose number
Calculate metrics for model predictive performance evaluation
Control options for non-compartmental analysis
Expand additional dosing (ADDL) records for pharmacokinetic analysis
Estimate the absorption rate constant using pointwise methods
Run and evaluate a one-compartment IV model
Run and evaluate a one-compartment IV Michaelis-Menten model
Run and evaluate a one-compartment oral model with Michaelis-Menten ki...
Run and evaluate a one-compartment oral model
Run and evaluate a two-compartment IV model
Run and evaluate a two-compartment oral model
Run and evaluate a three-compartment IV model
Run and evaluate a three-compartment oral model
Performs non-compartmental analysis on pooled data
Run full adaptive single-point PK analysis
Parameter sweeping for a one-compartment Michaelis-Menten model
Parameter sweeping for a two-compartment pharmacokinetic model
Parameter sweeping for a three-compartment pharmacokinetic model
Internal control builder for steady-state evaluation
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>.
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