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In Analytica release 4.4, the Smoothing option for PDF results uses KDE, and from expressions it is available via the built-in Pdf function. In C/C++, FIGTree is a library that can be used to compute kernel density estimates using normal kernels. MATLAB interface available. In C++, libagf is a library for variable kernel density estimation.
kde2d.m A Matlab function for bivariate kernel density estimation. libagf A C++ library for multivariate, variable bandwidth kernel density estimation. akde.m A Matlab m-file for multivariate, variable bandwidth kernel density estimation. helit and pyqt_fit.kde Module in the PyQt-Fit package are Python libraries for multivariate kernel density ...
kst-plot.kde.org Kst is a plotting and data viewing program. It is a general purpose plotting software program that evolved out of a need to visualize and analyze astronomical data, but has also found subsequent use in the real time display of graphical information.
In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.
Linux (KDE) The charting tool of Calligra Suite, an integrated graphic art and office suite developed by KDE. Kig: GUI: GPL: Yes 2006: Geometry diagrams only LabPlot: GUI, Qt, C, C++: GPL-2.0-or-later: Yes 2001: July 16, 2024 / 2.11.1 Microsoft Windows, OS X, Linux, FreeBSD, Haiku: 2D plotting, suitable for creation of publication-ready plots ...
free view-only version $50-$250/free v3.0 (academic) Proprietary: Visual language for simulation and Model Based Design. Used in business, science and engineering. Performs complex scalar or matrix based ODE solving with parametric optimization. Has 2D and 3D plotting, 3D animation, and state transition built in. Yorick: n/a n/a n/a 9 January ...
In statistics, especially in Bayesian statistics, the kernel of a probability density function (pdf) or probability mass function (pmf) is the form of the pdf or pmf in which any factors that are not functions of any of the variables in the domain are omitted. [1] Note that such factors may well be functions of the parameters of the
The generalized additive model for location, scale and shape (GAMLSS) is a semiparametric regression model in which a parametric statistical distribution is assumed for the response (target) variable but the parameters of this distribution can vary according to explanatory variables.