The PKNCA R package is designed to perform all noncompartmental analysis (NCA) calculations for pharmacokinetic (PK) data. The package is broadly separated into two parts (calculation and summary) with some additional housekeeping functions.

The primary and secondary goals of the PKNCA package are to 1) only give correct answers to the specific questions being asked and 2) automate as much as possible to simplify the task of the analyst. When automation would leave ambiguity or make a choice that the analyst may have an alternate preference for, it is either not used or is possible to override.

Note that backward compatibility will not be guaranteed until version 1.0. Argument and function changes will continue until then. These will be especially noticeable around the inclusion of IV NCA parameters and additional specifications of the dosing including dose amount and route.

# Citation

Citation information for the PKNCA package is available with a call to citation(package="PKNCA"). The preferred citation until publication of version 1.0 is below:

Denney W, Duvvuri S and Buckeridge C (2015). “Simple, Automatic Noncompartmental Analysis: The PKNCA R Package.” Journal of Pharmacokinetics and Pharmacodynamics, 42(1), pp. 11-107,S65. ISSN 1573-8744, doi: 10.1007/s10928-015-9432-2, <URL: https://github.com/billdenney/pknca>.

# Installation

## From CRAN

The current stable version of PKNCA is available on CRAN. You can install it and its dependencies using the following command:

install.packages("PKNCA")

## From GitHub

To install the development version from GitHub, type the following commands:

install.packages("remotes")
remotes::install_github("billdenney/pknca")

# Calculating parameters

# Load the package
library(PKNCA)
# Set the business rule options with the PKNCA.options() function
# Put your concentration data into a PKNCAconc object
o_conc <- PKNCAconc(data=conc_raw,
formula=conc~time|treatment+subject/analyte)
formula=dose~time|treatment+subject)
# Combine the two (and automatically determine the intervals of
# interest
# Compute the NCA parameters
o_results <- pk.nca(o_data)
# Summarize the results
summary(o_results)

More help is available in the function help files, and be sure to look at the PKNCA.options function for many choices to make PKNCA conform to your company’s business rules for calculations and summarization.

# Feature requests

Please use the github issues page (https://github.com/billdenney/pknca/issues) to make feature requests and bug reports.