kdensity 1.0.0

Kernel Density Estimation with Parametric Starts and Asymmetric Kernels

Released Feb 27, 2018 by Jonas Moss

This package can be loaded by Renjin but 4 out 6 tests failed.


knitr 1.20 EQL 1.0-0 assertthat 0.2.0 rmarkdown 1.10

Handles univariate non-parametric density estimation with parametric starts and asymmetric kernels in a simple and flexible way. Kernel density estimation with parametric starts involves fitting a parametric density to the data before making a correction with kernel density estimation, see Hjort & Glad (1995) . Asymmetric kernels make kernel density estimation more efficient on bounded intervals such as (0, 1) and the positive half-line. Supported asymmetric kernels are the gamma kernel of Chen (2000) , the beta kernel of Chen (1999) , and the copula kernel of Jones & Henderson (2007) . User-supplied kernels, parametric starts, and bandwidths are supported.



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Test Results

This package was last tested against Renjin 0.9.2689 on Aug 26, 2018.



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