Description
Population (and Individual) Optimal Experimental Design.
Description
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>.
README.md
PopED 
PopED computes 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 (FIM).
Installation
You need to have R installed. Download the latest version of R from www.r-project.org. You can install the released version of PopED from CRAN with:
install.packages("PopED")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("andrewhooker/PopED")
Getting started
To get started you need to define
- A model.
- An initial design (and design space if you want to optimize).
- The tasks to perform.
Learn more in this introduction to PopED
Contact
You are welcome to:
- submit suggestions and bug-reports at: https://github.com/andrewhooker/PopED/issues
- send a pull request on: https://github.com/andrewhooker/PopED
- compose a friendly e-mail to: [email protected].