You can find more information and validation in the Open Access paper.
Packaged: 2020-06-30 07:37:32 UTC; drbaranyailaszlo

Author: Patrick Meyer, Sébastien Bigaret.
Description: The MCDA package for the R statistical software allows you to use MCDA methods and functions in R.
Depends: R(>= 2.1.0), methods, Rglpk, Kendall, glpkAPI

Title: Functions to Support the Multicriteria Decision Aiding Process
Author: Patrick Meyer, Sébastien Bigaret, Richard Hodgett
Description: Functions which can be useful to support the analyst in the Multicriteria Decision Aiding (MCDA) process involving multiple, conflicting criteria.

Author: Patrick Meyer, Sébastien Bigaret, Richard Hodgett, Alexandru-Liviu Olteanu

Imports: Rglpk, glpkAPI, methods, RColorBrewer

Imports: Rglpk, glpkAPI, methods, RColorBrewer, combinat

Title: Support for the Multicriteria Decision Aiding Process
Description: Support for the analyst in a Multicriteria Decision Aiding (MCDA) process with algorithms,
preference elicitation and data visualisation functions. Sébastien Bigaret, Richard Hodgett, Patrick Meyer,
Tatyana Mironova, Alexandru Olteanu (2017) Supporting the multi-criteria decision aiding process :
R and the MCDA package, Euro Journal On Decision Processes, Volume 5, Issue 1 - 4,

Imports: Rglpk, glpkAPI, methods, RColorBrewer, combinat, triangle,

Title: Functions to support the Multicriteria Decision Aiding process
Description: MCDA, which stands for Multicriteria Decision Aiding, is an R package implementing functions which can be useful to support the analyst in the decision aiding process involving multiple, conflicting criteria. These functions include, but are not limited to, data pre-processing tools, preference elicitation procedures, aggregation functions, as well as post-processing techniques and data visualisation functions.

Description: MCDA, which stands for Multicriteria Decision Aiding, implements functions which can be useful to support the analyst in the decision aiding process involving multiple, conflicting criteria. These functions include, but are not limited to, data pre-processing tools, preference elicitation procedures, aggregation functions, as well as post-processing techniques and data visualisation functions.