#simulate a dataset with continuous data
dataset <- matrix(runif(200 * 80, 1, 1000), nrow = 200 )
#the target feature is the last column of the dataset as a vector
target <- dataset[, 80]
dataset <- dataset[, -80]
target <- target / (max(target) + 2 )
results <- testIndBeta(target, dataset, xIndex = 44, csIndex = 60)
results
#require(gRbase) #for faster computations in the internal functions
#define class variable (here tha last column of the dataset)
target = 50;
#run the SES algorithm using the testIndBeta conditional independence test
sesObject <- SES(target, dataset, max_k = 3, threshold = 0.05, test = "testIndBeta");
#print summary of the SES output
summary(sesObject);
#plot the SES output
plot(sesObject, mode = "all");
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