Package: prefeR 0.1.3

John Lepird

prefeR: R Package for Pairwise Preference Elicitation

Allows users to derive multi-objective weights from pairwise comparisons, which research shows is more repeatable, transparent, and intuitive other techniques. These weights can be rank existing alternatives or to define a multi-objective utility function for optimization.

Authors:John Lepird

prefeR_0.1.3.tar.gz
prefeR_0.1.3.zip(r-4.5)prefeR_0.1.3.zip(r-4.4)prefeR_0.1.3.zip(r-4.3)
prefeR_0.1.3.tgz(r-4.4-any)prefeR_0.1.3.tgz(r-4.3-any)
prefeR_0.1.3.tar.gz(r-4.5-noble)prefeR_0.1.3.tar.gz(r-4.4-noble)
prefeR_0.1.3.tgz(r-4.4-emscripten)prefeR_0.1.3.tgz(r-4.3-emscripten)
prefeR.pdf |prefeR.html
prefeR/json (API)

# Install 'prefeR' in R:
install.packages('prefeR', repos = c('https://jlepird.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/jlepird/prefer/issues

On CRAN:

bayesian-inferencemultiobjective-optimizationpreference-elicitation

3.88 score 1 stars 15 scripts 129 downloads 9 exports 2 dependencies

Last updated 3 years agofrom:ed69536e80. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 14 2024
R-4.5-winOKOct 14 2024
R-4.5-linuxOKOct 14 2024
R-4.4-winOKOct 14 2024
R-4.4-macOKOct 14 2024
R-4.3-winOKOct 14 2024
R-4.3-macOKOct 14 2024

Exports:%<%%=%%>%ExpFlatinferNormalprefElsuggest

Dependencies:entropymcmc

Preference Elicitation on Motor Trends Dataset

Rendered frommtcars.Rmdusingknitr::rmarkdownon Oct 14 2024.

Last update: 2017-02-20
Started: 2016-06-05