This module contains a small but growing library for performing kernel density estimation.
|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.|
|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.|