ANOVA Using Continuous CDFs
Per the answer to this question, storing the variables into a matrix works.

VN_ANOVA&lt;- function(A){

  mean_of_means &lt;- mean(colMeans(A))
  n&lt;- ncol(A)

  LPM_ratio = numeric(0L)
  MAD_CDF = numeric(0L)


#Continuous CDF for each variable from Mean of Means  
  for (i in 1:n){
      LPM_ratio[i] &lt;- LPM(1,mean_of_means,A[,i])/
                 (LPM(1,mean_of_means,A[,i])+UPM(1,mean_of_means,A[,i]))

#Continuous CDF Deviation from 0.5
      MAD_CDF[i]&lt;- abs(LPM_ratio[i] - 0.5)
      }

  Mean_MAD_CDF &lt;- mean(MAD_CDF)


#Certainty associated with samples
  VN_ANOVA_rho &lt;- (0.5 - Mean_MAD_CDF)/0.5


#Graphs  
  boxplot(A, las=2, xlab= "Means", horizontal = TRUE,
       main="ANOVA", col=rainbow(n))


#For ANOVA Visualization      
  abline(v=mean_of_means,col="red",lwd=4)
  text(mean_of_means,pos=4, .25,"Mean of means", col = "red")


  return(c("Certainty of Same Population"=VN_ANOVA_rho))

}