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ks: Kernel density estimation and kernel discriminant analysis for multivariate data in R
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DOI：10.18637/jss.v021.i07.png)
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摘要
En 中文
Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing. Currently it contains functionality for kernel density estimation and kernel discriminant analysis. It is a comprehensive package for bandwidth matrix selection, implementing a wide range of data-driven diagonal and unconstrained bandwidth selectors.
Keyword:
bandwidth selection
data-driven
non-parametric smoothing
