The function entropy of package infotheo takes the dataset as input and computes the entropy according to the entropy estimator method. For example:
data(USArrests)
H <- entropy(discretize(USArrests),method="shrink")
returns, for H, a value of 3.433987. But in this case data(USArrests) is formed by 4 columns (variables), hence I think that it is not an entropy (which usually refers to a single variable) but a joint entropy. It is not explained in the package documentation. I tried to check the content of function entropy but I obtained:
> infotheo::entropy
function (X, method = "emp") 
{
   X <- data.frame(X)
   X <- data.matrix(X)
   n <- NCOL(X)
   N <- NROW(X)
   Z <- na.omit(X)
   if (!(all(Z == round(Z)))) 
        stop("This method requires discrete values")
   res <- NULL
   if (method == "emp") 
        choi <- 0
   else if (method == "mm") 
        choi <- 1
   else if (method == "sg") 
        choi <- 2
    else if (method == "shrink") 
        choi <- 3
    else stop("unknown method")
    res <- .Call("entropyR", X, N, n, choi, PACKAGE = "infotheo")
    res
}
<bytecode: 0x000000000ae40c50>
<environment: namespace:infotheo>
From this code I can't understand how the result H has been computed.
