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ncappc: NCA Calculations and population model diagnosis

ncappc performs NCA Calculations and population model diagnosis using posterior predictive checks generated from data simulated by a population model.

ncappc is a flexible tool that can perform:

  1. Traditional non-compartmental analysis (NCA) and
  2. Simulation-based posterior predictive checks for population pharmacokinetic (PK) and/or pharmacodynamic (PKPD) models using NCA metrics.

You can read more at the website for the stable version of ncappc or the website for the development version of ncappc

Installation

You need to have R installed. Download the latest version of R from www.r-project.org.

You can install the latest stable release from CRAN:

install.packages("ncappc")

You can install the development version of ncappc from GitHub with:

# install.packages("pak")
pak::pak("UUPharmacometrics/ncappc")

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Version

Install

install.packages('ncappc')

Monthly Downloads

319

Version

1.0.0

License

GPL-3

Issues

Pull Requests

Stars

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Maintainer

Andrew C. Hooker

Last Published

April 25th, 2025

Functions in ncappc (1.0.0)

nca.deviation.plot

Plot individual deviation of NCA metrics estimated from observed and simulated data
nca.npde

Calculates individual normalized prediction distribution errors (NPDE) from PDE data.
nca.check.obs

Check observed data
histobs.plot

Plots histogram of selected set of NCA metrics.
nca.check.sim

Check simulated data
dv.plot

Plots drug plasma concentration vs time data
calc.stat

Calculates a set of statistics for a given array of numbers.
est.nca

Estimate individual NCA metrics.
histpop.plot

Plots population histogram of the NCA metrics selected for model diagnosis.
nca_ind_data

Prepare individual PK data
nca.npde.plot

Plots population histogram of the NCA metrics selected for model diagnosis.
ncappc

Performs NCA calculations and population PK model diagnosis.
nca.pde.deviation.outlier

Calculates individual prediction distribution errors (PDE) and scaled deviation of NCA metrics estimated from observed and simulated data. Identifies outlier to population PK model.
nca.read.sim

Check observed data
out.digits

output value with correct digits and trailing zero
read_nm_table

Read NONMEM table files produced.
nca.ind.data

Prepare individual PK data