ConsRank (version 2.0.1)

ConsRank-package: Compute the Median Ranking According to the Kemeny's Axiomatic Approach

Description

Compute the median ranking according to the Kemeny's axiomatic approach. Rankings can or cannot contain ties, rankings can be both complete or incomplete. The package contains both branch-and-bound and heuristic solutions

Details

Package:	ConsRank
Type:	Package
Version:	2.0.1
Date:	2017-04-28
License:	GPL-3

References

Kemeny, J. G., & Snell, J. L. (1962). Mathematical models in the social sciences (Vol. 9). New York: Ginn.

Marden, J. I. (1996). Analyzing and modeling rank data. CRC Press.

Emond, E. J., & Mason, D. W. (2002). A new rank correlation coefficient with application to the consensus ranking problem. Journal of Multi-Criteria Decision Analysis, 11(1), 17-28.

D'Ambrosio, A. (2008). Tree based methods for data editing and preference rankings. Ph.D. thesis. http://www.fedoa.unina.it/id/eprint/2746

Heiser, W. J., & D'Ambrosio, A. (2013). Clustering and prediction of rankings within a Kemeny distance framework. In Algorithms from and for Nature and Life (pp. 19-31). Springer International Publishing.

Amodio, S., D'Ambrosio, A. & Siciliano, R (2016). Accurate algorithms for identifying the median ranking when dealing with weak and partial rankings under the Kemeny axiomatic approach. European Journal of Operational Research, vol. 249(2).

D'Ambrosio, A., Amodio, S. & Iorio, C. (2015). Two algorithms for finding optimal solutions of the Kemeny rank aggregation problem for full rankings. Electronic Journal of Applied Statistical Analysis, vol. 8(2).

D'Ambrosio, A., Mazzeo, G., Iorio, C., & Siciliano, R. (2017). A differential evolution algorithm for finding the median ranking under the Kemeny axiomatic approach. Computers & Operations Research, vol. 82.

Examples

# NOT RUN {
## load APA data set, full version
data(APAFULL)
## Emond and Mason Branch-and-Bound algorithm. 
## If the number of object is higher than 20, EMCons function may work for several minutes. 
## Use either QuickCons, DECOR, FASTcons or FASTDECOR instead
CR=EMCons(APAFULL)
TR=tabulaterows(APAFULL)
CR2=FASTcons(TR$X,TR$Wk,maxiter=5)
CR3=QuickCons(TR$X,TR$Wk)
CR4=DECOR(TR$X,TR$Wk)

#####################################
### load sports data set
#data(sports)
### FAST algorithm
#CR=FASTcons(sports,maxiter=10)
#####################################

#######################################
### load Emond and Mason data set
#data(EMD)
### matrix X contains rankings
#X=EMD[,1:15]
### vector Wk contains frequencies
#Wk=EMD[,16]
### QUICK algorithm
#CR=QuickCons(X,Wk)
#######################################

# }