Optimal experimental designs for both population and individual studies based on nonlinear mixed-effect models. Often this is based on a computation of the Fisher Information Matrix. This package was developed for pharmacometric problems, and examples and predefined models are available for these types of systems. The methods are described in Nyberg et al. (2012) <doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004) <doi:10.1016/S0169-2607(03)00073-7>.
Version: 0.7.0 Depends: R (≥ 2.14) Imports: ggplot2, MASS, mvtnorm, dplyr (≥ 0.7.0), codetools, stats, utils, magrittr, boot, purrr, stringr, tibble, gtools Suggests: testthat, Hmisc, nlme, GA, deSolve, Rcpp, shiny, rhandsontable, knitr, rmarkdown, gridExtra, covr, devtools, mrgsolve Published: 2024-10-07 DOI: 10.32614/CRAN.package.PopED Author: Andrew C. Hooker [aut, cre, trl, cph], Marco Foracchia [aut] (O-Matrix version), Eric Stroemberg [ctb] (MATLAB version), Martin Fink [ctb] (Streamlining code, added functionality, vignettes), Giulia Lestini [ctb] (Streamlining code, added functionality, vignettes), Sebastian Ueckert [aut] (MATLAB version), Joakim Nyberg [aut] (MATLAB version) Maintainer: Andrew C. Hooker <andrew.hooker at farmaci.uu.se> BugReports: https://github.com/andrewhooker/PopED/issues License: LGPL (≥ 3) Copyright: 2014-2021 Andrew C. Hooker URL: https://andrewhooker.github.io/PopED/, https://github.com/andrewhooker/PopED NeedsCompilation: no Citation: PopED citation info Materials: README NEWS In views: ExperimentalDesign, Pharmacokinetics CRAN checks: PopED results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=PopED to link to this page.
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