---
title: "mcdabench: Benchmarking for Multi-Criteria Decision Analysis"
author: "Cagatay Cebeci"
date: "2026-04-29"
#output: html_document
---

# Introduction

Implements various Multi-Criteria Decision Analysis (MCDA) methods for benchmarking studies. These methods are designed to evaluate and rank alternatives based on 
multiple criteria, applying normalization, weighting, and aggregation techniques. The package includes a new proposed algorithm MEGAN in addition to the popular decision-making methods 
such as  ARAS, AROMAN, COCOSO, CODAS, COPRAS, EDAS, ELECTRE family (I-IV), FUCA, GRA, MABAC, MAIRCA, MARCOS, MAUT, MAVT, MEGAN, MOORA, OCRA, ORETES, PROMETHEE family (I - VI), 
RAM, ROV, SMART, TOPSIS, VIKOR, WASPAS, WPM, WSM and many more, facilitating flexible and efficient analyses for multi-criteria problems.

# Install mcdabench

You can easily install the `mcdabench` package from CRAN:

```r
install.packages("mcdabench", dep=TRUE)
```

After installing the package, load it into your R session by running:
library(mcdabench)

```r
library(mcdabench)
```

# Quickly mcdabench
## Load data
```r
data(egrids)
dmat < egrids$dmat # Decision matrix
bc <- egrids$bcvec # Benefit-cost vector
uw <- egrids$weights # Criteria weights 

## Normalization
```r
nmatrix1 <- normalize(dmatrix, bcvec=bc, type="maxmin")
nmatrix1

nmatrix2 <- normalize(dmatrix, bcvec=bc, type="sum")
nmatrix2
```

## Calculate weights
```r
equwei <- calcweights(nmatrix1, bcvec=bc, type="equal")
equwei

entwei <- calcweights(nmatrix1, bcvec=bc, type="entropy")
entwei
```

## Apply MCDA Methods
### Run MEGAN
```r
resmegan <- megan(dmatrix, bcvec=bc, weights=uw, normethod="maxmin")
print(resmegan)$rank
```

### Run TOPSIS
```r
restopsis <- topsis(dmatrix, bcvec=bc, weights=uw, normethod="maxmin")
print(restopsis)
```

### Run VIKOR
```r
resvikor <- vikor(dmatrix, bcvec=bc, weights=uw, v=0.8)
print(resvikor$rank)
```

## Weight Sensitivity Analsyis
### Gradual weight modification for Testing VIKOR
```r
mp <- list(v=0.5)
wp <- list(rp = seq(0.01, 0.5, 0.05))

vikorgrawei <- weisana(dmatrix = dmat, bcvec = bc,
     weimethod = "gradual", weipars = wp,
     mcdamethod = vikor, methodpars = mp, sensplot=FALSE)
print(vikorgrawei)
sensplot(vikorgrawei$sensitivity_table, 
   mtitle="Weight Sensivity Analysis for VIKOR", colpal=terrain.colors(10))
```

### Test WASPAS with gradual and random weighting
```r
mp <- list(v=0.5, normethod="linear", tiesmethod="average")
wp <- list(rp = seq(0.01, 0.6, 0.01))

waspasgrawei <- weisana(dmatrix = dmat, bcvec = bc,
     weimethod = "gradual", weipars = wp,
     mcdamethod = waspas, methodpars = mp)
print(waspasgrawei)
sensplot(waspasgrawei$sensitivity_table, 
   mtitle="Weight Sensivity Analysis for WASPAS", colpal=terrain.colors(10))
   
waspasrandwei <- weisana(dmatrix = dmat, bcvec = bc,
     weimethod = "random", weipars = list(ss=0.05, niters=50),
     mcdamethod = waspas, methodpars = mp)
print(waspasrandwei)
sensplot(waspasrandwei$sensitivity_table, 
   mtitle="Weight Sensivity Analysis (random) for WASPAS", colpal=terrain.colors(10))
```

# Bechmarking the Methods
```r
# Sample decision matrix
dm <- matrix(c(
  10, 20, 30, 1.5, 102, 55,
  15, 25, 35, 1.6, 90, 60,
  12, 22, 32, 1.7, 100, 58,
  13, 24, 33, 1.8, 95, 57,
  14, 26, 37, 1.9, 98, 59,
  11, 23, 31, 1.65, 101, 56,
  16, 27, 36, 1.55, 97, 61,
  17, 28, 38, 1.7, 99, 63,
  18, 29, 39, 1.8, 94, 62,
  19, 30, 40, 1.75, 96, 64
), nrow = 10, byrow = TRUE)
colnames(dm) <- paste0("C", 1:ncol(dm))
rownames(dm) <- paste0("ALT", 1:nrow(dm))

# Benefit-Cost vector
bc <- c(1, -1, 1, -1, 1, 1)

# User-defined weights
userwei <- c(0.3, 0.1, 0.2, 0.1, 0.2, 0.1)

prmlist <- list(
      aras      = list(),
      aroman    = list(lambda = 0.5, beta = 0.5),
      cocoso    = list(lambda = 0.5),
      codas     = list(thr = 0.1),  
      smart     = list(),
      topsis    = list(normethod = "maxmin"),
      vikor     = list(normethod = "maxmin", v = 0.8),
      waspas    = list(normethod = "linear", v = 0.5),
      wpm       = list(normethod = "vector"),
      wsm       = list()
)

# Compare selected methods with 'gini' weights
giniwei <- calcweights(dm, bcvec=bc, type="gini")
rescomp_3 <- methodbench(dmatrix = dm, bcvec = bc, weights = giniwei,
   mcdm = methodlist, params=prmlist)
print(rescomp_3$rankmat)
rankheatmap(rescomp_3$rankmat, colpal=1, cellnotes=TRUE, tcol="white")

# Overall ranks and outranking
resoverall <- rankaggregate(rescomp_3$rankmat, tiesmethod="average", topk = 3,
   damp = 0.5, niters = 200, tol = 1e-4)
print(resoverall)
```

# Citation

To cite the mcdabench package in publications, please run the following code in R.

```r 
citation("mcdabench")
```

