Supports designing efficient discrete choice experiments (DCEs). Experimental designs can be formed on the basis of orthogonal arrays or search methods for optimal designs (Federov or mixed integer programs). Various methods for converting these experimental designs into a discrete choice experiment. Many efficiency measures! Draws from literature of Kuhfeld (2010) and Street et. al (2005) <doi:10.1016/j.ijresmar.2005.09.003>.
Version: | 0.2.0 |
Depends: | R (≥ 3.6.0) |
Imports: | stats, far, dplyr, DoE.base, rlist, purrr |
Suggests: | knitr, rmarkdown |
Published: | 2020-04-03 |
Author: | Jed Stephens [aut, cre] |
Maintainer: | Jed Stephens <STPJED001 at myuct.ac.za> |
BugReports: | https://github.com/JedStephens/ExpertChoice/issues |
License: | MIT + file LICENSE |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | ExpertChoice results |
Reference manual: | ExpertChoice.pdf |
Vignettes: |
Practical introduction to ExpertChoice Theoretical introduction to ExpertChoice |
Package source: | ExpertChoice_0.2.0.tar.gz |
Windows binaries: | r-devel: ExpertChoice_0.2.0.zip, r-release: ExpertChoice_0.2.0.zip, r-oldrel: ExpertChoice_0.2.0.zip |
macOS binaries: | r-release (arm64): ExpertChoice_0.2.0.tgz, r-oldrel (arm64): ExpertChoice_0.2.0.tgz, r-release (x86_64): ExpertChoice_0.2.0.tgz, r-oldrel (x86_64): ExpertChoice_0.2.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=ExpertChoice to link to this page.