library.smooth Submodule

Module Summary

Interfaces for the NAG Mark 27.1 smooth Chapter.

smooth - Smoothing in Statistics

This module is concerned with methods for smoothing data. Included are methods for density estimation, smoothing time series data, and statistical applications of splines. These methods may also be viewed as nonparametric modelling.

Functionality Index

Compute smoothed data sequence

running median smoothers: data_runningmedian()

Fit cubic smoothing spline

smoothing parameter estimated: fit_spline_parest()

smoothing parameter given: fit_spline()

Kernel density estimation

Gaussian kernel, thread safe: kerndens_gauss()

Reorder data to give ordered distinct observations: data_order()

For full information please refer to the NAG Library document

https://www.nag.com/numeric/nl/nagdoc_27.1/flhtml/g10/g10intro.html