Provides a methodology to solve most of multicriteria ranking problems using partial and total pre-order from Promethee methods. Albuquerque & Montenegro (2015)
RMCriteria is a package to solve Multiple-Criteria Decision Analysis (MCDA) problems. For now, it only supports Promethee methods, but other methods may be developed in the future.
Currently, the easiest way to install
RMCriteria is through
devtools package, straight from GitHub:
RMCriteria is quite simple. The general idea is to create a
RPrometheeArguments object, with all parameters such as the criterias and alternatives and then applying this object to the chosen method, like
## Create objects for each argumentdata <-matrix(c(5.2,-3.5,4.3,-1.2,6.7,-2.0), byrow = T, ncol=2, nrow=3)parms <- matrix(c(NA,NA),byrow=TRUE,ncol=1,nrow=2)vecWeights <- c(0.3,0.7)vecMaximiz <- c(F,T)prefFunction <- c(0,0)normalize <- FALSEalternatives <- c("Alt 1", "Alt 2", "Alt 3")## Create RPrometheeArguments objectPromObj <- RPrometheeConstructor(datMat = data, vecWeights = vecWeights, vecMaximiz = vecMaximiz, prefFunction = prefFunction, parms = parms, normalize = normalize, alternatives = alternatives)# Run RPrometheeI(result <- RPrometheeI(PromObj))
RMCriteria was developed in the Laboratory of Machine Learning in Finance and Organizations (LAMFO) from University of Brasilia, in Brazil. LAMFO is a center devoted to research machine learning methods and related subjects applied to organizations in Marketing, Finance, Logistics and many others.