Given an \(n \times d\) matrix \(x\) of points in
\(R^d\), this function removes duplicated observations, and
counts the number of times each observation occurs. This is used to
compute a vector \(w\) such that $$w_i = \frac{\# \textrm{ of
times value } i\textrm{ is observed }}{\# \textrm{ of
observations}}.$$
This function is called by mlelcd in order to compute
the maximum likelihood estimator when the observed data values are not
distinct. In this case, the log likelihood function is of the form
$$\sum_{j=1}^m w_j \log f(X_j),$$
where the sum is over distinct observations.