This module contains a small but growing library for performing kernel density estimation.
Construct an N-dimensional kernel density estimator. This is done using the textbook definition of kernel density estimation, since the binning and convolving method used in the 1-D case would rapidly become unfeasible w.r.t. memory usage as dimensionality increased.
Estimates densities in the 1-dimensional case. The 1-D case is special enough to be treated as a special case, since it's very common and enables some significant optimizations that are otherwise not feasible.
Uses Scott's Rule to select the bandwidth of the Gaussian kernel density estimator. This is 1.06 * min(stdev(data), interquartileRange(data) / 1.34) N ^^ -0.2. R must be a forward range of numeric types.