Kernel density estimation: Difference between revisions

Revision as of 09:26, 6 May 2025

Latest revision as of 21:17, 9 August 2025

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* In [[Haskell (programming language)|Haskell]], kernel density is implemented in the [http://hackage.haskell.org/package/statistics statistics] package.

* In [[IGOR Pro]], kernel density estimation is implemented by the <code>StatsKDE</code> operation (added in Igor Pro 7.00). Bandwidth can be user specified or estimated by means of Silverman, Scott or Bowmann and [[Adelchi Azzalini|Azzalini]]. Kernel types are: Epanechnikov, Bi-weight, Tri-weight, Triangular, Gaussian and Rectangular.

* In [[Java (programming language)|Java]], the [[Weka (machine learning)|Weka]] machine learning package provides [httphttps://weka.sourceforge.net/doc.stable/weka/estimators/KernelEstimator.html weka.estimators.KernelEstimator], among others.

* In [[JavaScript]], the visualization package [[D3js|D3.js]] offers a KDE package in its science.stats package.

* In [[JMP (statistical software)|JMP]], the Graph Builder platform utilizes kernel density estimation to provide contour plots and high density regions (HDRs) for bivariate densities, and violin plots and HDRs for univariate densities. Sliders allow the user to vary the bandwidth. Bivariate and univariate kernel density estimates are also provided by the Fit Y by X and Distribution platforms, respectively.

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* In [[Minitab]], the Royal Society of Chemistry has created a macro to run kernel density estimation based on their Analytical Methods Committee Technical Brief 4.<ref>{{Cite web|title=Software for calculating kernel densities|url=https://www.rsc.org/Membership/Networking/InterestGroups/Analytical/AMC/Software/kerneldensities.asp|access-date=2020-11-05|website=www.rsc.org}}</ref>

* In the [[NAG Numerical Library|NAG Library]], kernel density estimation is implemented via the <code>g10ba</code> routine (available in both the Fortran<ref>{{cite web |last=The Numerical Algorithms Group |title=NAG Library Routine Document: nagf_smooth_kerndens_gauss (g10baf) |work=NAG Library Manual, Mark 23 |url=http://www.nag.co.uk/numeric/fl/nagdoc_fl23/pdf/G10/g10baf.pdf |access-date=2012-02-16 }}</ref> and the C<ref>{{cite web |last=The Numerical Algorithms Group |title=NAG Library Routine Document: nag_kernel_density_estim (g10bac) |work=NAG Library Manual, Mark 9 |url=http://www.nag.co.uk/numeric/CL/nagdoc_cl09/pdf/G10/g10bac.pdf |access-date=2012-02-16 |archive-url=https://web.archive.org/web/20111124062333/http://nag.co.uk/numeric/cl/nagdoc_cl09/pdf/G10/g10bac.pdf |archive-date=2011-11-24 |url-status=dead }}</ref> versions of the Library).

* In [httphttps://nuklei.sourceforge.net/ Nuklei], [[C++]] kernel density methods focus on data from the Special Euclidean group <math>SE(3)</math>.

* In [[GNU Octave|Octave]], kernel density estimation is implemented by the <code>kernel_density</code> option (econometrics package).

* In [[Origin (data analysis software)|Origin]], 2D kernel density plot can be made from its user interface, and two functions, Ksdensity for 1D and Ks2density for 2D can be used from its [http://wiki.originlab.com/~originla/ltwiki/index.php?title=Category:LabTalk_Programming LabTalk], [[Python (programming language)|Python]], or [[C (programming language)|C]] code.