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#' @title mmpc algorithm with additive noise model
#' @description The nonlinear data comparison algorithm. We use the mmpc algorithm to learn a causal skeleton and use ANM to recognize the direction
#' @param data The data
#' @export
mmpcAnm<-function(data){
fitG=mmpc(data,test="mi-g-sh") #shrinkage estimator for the mutual information
fitG=amat(fitG)
n=ncol(fitG)
pb <- txtProgressBar(0,sum(fitG==1)/2,style = 3)
count=0
for(i in 1:n){
for(j in 1:n){
if(fitG[i,j]==1&&fitG[j,i]==1){
count=count+1
setTxtProgressBar(pb,count)
X=data[,c(i,j)]
fit<-getParents(X=X,method = "bivariateANM")
if(fit[1,2]==1){ #if i to j
fitG[j,i]=0
}else if(fit[2,1]==1){
fitG[i,j]=0
}else{
fitG[j,i]=0
fitG[i,j]=0
}
}
}
}
return(fitG)
}
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