testthat, re2r, stringi, ggplot2, tidyr (>= 1.0.0), cdata,
reshape2, knitr, R.utils

testthat, re2r, stringi, ggplot2, tidyr (>= 1.0.0), cdata,
reshape2, knitr, markdown, R.utils

NCA Calculation and Population PK Model Diagnosis
c(person("Chayan", "Acharya", role = c("aut", "cre"), email =
"chayan.acharya@farmbio.uu.se"), person("Andrew C.", "Hooker", role =
"aut"), person("Siv", "Jonsson", role = "aut"), person("Mats O.",
"Karlsson", role = "aut"))
A flexible tool is presented here that can perform
(i) traditional non-compartmental analysis (NCA) and
(ii) simulation-based posterior predictive checks for a population
pharmacokinetic (PK) model using NCA metrics.
ggplot2, grid, gridExtra, scales, gtable,
PerformanceAnalytics, lattice, knitr, xtable, reshape2
Chayan Acharya [aut, cre],
Andrew C. Hooker [aut],
Siv Jonsson [aut],
Mats O. Karlsson [aut]

scales, gtable, knitr, xtable, reshape2, testthat, dplyr,
readr (>= 0.2.2), lazyeval

scales, gtable, knitr, xtable, reshape2, testthat

NCA Calculations and Population Model Diagnosis
c(person("Chayan", "Acharya", role = "aut"),
person("Andrew C.", "Hooker", role = c("aut", "cre"), email = "andrew.hooker@farmbio.uu.se"),
person("Gulbeyaz Y.", "Turkyilmaz", role = "aut"),
person("Siv", "Jonsson", role = "aut"),
person("Mats O.", "Karlsson", role = "aut"))
A flexible tool that can perform
(i) traditional non-compartmental analysis (NCA) and
(ii) Simulation-based posterior predictive checks for population
pharmacokinetic (PK) and/or pharmacodynamic (PKPD) models using NCA metrics.
scales, gtable, knitr, xtable, reshape2, dplyr, Cairo, readr
(>= 0.2.2), lazyeval, PopED, magrittr, rlang, purrr, tibble,
rmarkdown, tidyr, ggplot2 (>= 2.0.0), grid, gridExtra (>=
2.0.0), grDevices, stats, bookdown
Chayan Acharya [aut],
Andrew C. Hooker [aut, cre],
Gulbeyaz Y. Turkyilmaz [aut],
Siv Jonsson [aut],
Mats O. Karlsson [aut]

Noncompartmental Analysis of Pharmacokinetic Data for CDISC
Standardized Parameters
Kyun-Seop Bae [aut, cre], Sungpil Han [aut]
Conduct a noncompartmental analysis as closely as possible to the most widely used commercial software for pharmacokinetic analysis, i.e. 'Phoenix(R) WinNonlin(R)' <https://www.certara.com/software/pkpd-modeling-and-simulation/phoenix-winnonlin/>.
Some features include:
1) Use CDISC SDTM PP domain terms and generate PP domains from DM, EX, and PC domains.
2) Automatic slope selection with the same criterion of WinNonlin(R)
3) Support both 'linear-up linear-down' and 'linear-up log-down' method
4) Calculate partial(interval) AUC with 'linear' or 'log' interpolation method
5) Perform a noncompartmental analysis of CDISC standardized pharmacokinetic dataset (.XPT)
For more details on noncompartmental analysis, see the reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107)
*) Acknowledgement: Author thanks for the careful review and valuable input of Dr. Jee Eun Lee.
dplyr, tidyr, Hmisc, lubridate, measurements