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nlmixr2autoinit (version 1.0.0)

Automatic Generation of Initial Estimates for Population Pharmacokinetic Modeling

Description

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) .

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Install

install.packages('nlmixr2autoinit')

Monthly Downloads

224

Version

1.0.0

License

GPL (>= 3)

Issues

5

Pull Requests

2

Stars

6

Forks

1

Maintainer

Zhonghui Huang

Last Published

November 13th, 2025

Functions in nlmixr2autoinit (1.0.0)

getsigma

Compute overall residual variability from elimination phase
getsigmas

Estimate individual-level residual error from the elimination phase
getnca

Perform non-compartmental pharmacokinetic analysis
get_pooled_data

Generate pooled data for pharmacokinetic analysis
initsControl

Create full control list for initial parameter estimation
hybrid_eval_perf_1cmpt

Generate Unique Mixture Parameter Grid (with Deduplication and NA Removal)
ka_calculation_sd

Estimate absorption rate constant in a one-compartment oral model
ka_wanger_nelson

Calculate the absorption rate constant using the Wagner-Nelson method
nmpkconvert

Expand additional dosing (ADDL) records for pharmacokinetic analysis
nca_control

Control options for non-compartmental analysis
graphcal_iv

Graphical calculation of clearance and volume of distribution (IV route)
graphcal_oral

Graphical calculation of pharmacokinetic parameters for oral administration
pooled_control

Control settings for pooled data analysis
print.getPPKinits

Print method for getPPKinits objects
run_graphcal

Run graphical analysis of pharmacokinetic parameters
processData

Process time–concentration dataset for pharmacokinetic analysis
calculate_tad

Calculate time after dose for pharmacokinetic data
run_npd_2cmpt_iv

Run and evaluate a two-compartment IV model
run_npd_1cmpt_oral

Run and evaluate a one-compartment oral model
run_npd_1cmpt_mm_oral

Run and evaluate a one-compartment oral model with Michaelis-Menten kinetics
run_npd_1cmpt_mm_iv

Run and evaluate a one-compartment IV Michaelis-Menten model
is_ss

Determine steady state for pharmacokinetic observations
run_npd_2cmpt_oral

Run and evaluate a two-compartment oral model
ka_calculation_md

Calculate absorption rate constant (ka) in a multiple-dose one-compartment model
mark_dose_number

Mark dose number
metrics.

Calculate metrics for model predictive performance evaluation
run_npd_1cmpt_iv

Run and evaluate a one-compartment IV model
run_single_point

Run full adaptive single-point PK analysis
run_ka_solution

Estimate the absorption rate constant using pointwise methods
run_single_point_base

Run adaptive single-point pharmacokinetic analysis
trimmed_geom_mean

Computes the trimmed geometric mean
run_pooled_nca

Performs non-compartmental analysis on pooled data
trapezoidal_linear_up_log_down

Linear-up and log-down trapezoidal rule
run_npd_3cmpt_iv

Run and evaluate a three-compartment IV model
run_npd_3cmpt_oral

Run and evaluate a three-compartment oral model
sim_sens_3cmpt

Parameter sweeping for a three-compartment pharmacokinetic model
sim_sens_2cmpt

Parameter sweeping for a two-compartment pharmacokinetic model
trapezoidal_linear

Linear trapezoidal rule
ss_control

Internal control builder for steady-state evaluation
run_single_point_extra

Perform extended single-point pharmacokinetic calculations
sim_sens_1cmpt_mm

Parameter sweeping for a one-compartment Michaelis-Menten model
Fit_1cmpt_mm_oral

Fit oral pharmacokinetic data to a one-compartment model with Michaelis-Menten elimination
Fit_1cmpt_mm_iv

Fit intravenous pharmacokinetic data to a one-compartment model with Michaelis-Menten elimination
Fit_3cmpt_iv

Fit intravenous pharmacokinetic data to a three-compartment linear elimination model
Fit_2cmpt_iv

Fit intravenous pharmacokinetic data to a two-compartment linear elimination model
approx.vc

Approximate volume of distribution from observed Cmax
Fit_1cmpt_oral

Fit oral pharmacokinetic data to a one-compartment linear elimination model
Fit_2cmpt_oral

Fit oral pharmacokinetic data to a two-compartment model
Fit_3cmpt_oral

Fit oral pharmacokinetic data to a three-compartment linear elimination model
bin.time

Bin time-concentration data using quantile or algorithmic binning
Fit_1cmpt_iv

Fit intravenous pharmacokinetic data to a one-compartment linear elimination model
fallback_control

Control settings for fallback rules in parameter estimation
force_find_lambdaz

Forceful estimation of terminal slope
get_hf

Estimate half-life from pooled pharmacokinetic data
eval_perf_1cmpt

Evaluates predictive performance of a one-compartment model
find_best_lambdaz

Find the best terminal elimination rate constant (lambdaz)
calculate_cl

Calculate clearance using an adaptive single-point method
calculate_vd

Calculates volume of distribution from concentration data
getPPKinits

Automated pipeline for generating initial estimates in population PK models
getOmegas

Generate ETA variance and covariance table