roughness_K: Kernel roughness
In FKSUM: Fast Kernel Sums
roughness_K R Documentation
Kernel roughness
Description
Computes the squared L2 norm, also known as the roughness, of a kernel implemented in FKSUM based on its beta coefficients. NB: the
coefficients will first be normalised so that the kernel represents a density function. Equivalent to norm_K()^2
Usage
roughness_K(beta)
Arguments
beta
numeric vector of positive coefficients.
Value
positive numeric representing the squared L2 norm, or roughness of the kernel with coefficients beta/norm_const(beta).
References
Hofmeyr, D.P. (2021) "Fast exact evaluation of univariate kernel sums", IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(2), 447-458.
FKSUM documentation built on April 15, 2023, 5:06 p.m.
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| roughness_K | R Documentation |
Kernel roughness
Description
Computes the squared L2 norm, also known as the roughness, of a kernel implemented in FKSUM based on its beta coefficients. NB: the coefficients will first be normalised so that the kernel represents a density function. Equivalent to norm_K()^2
Usage
roughness_K(beta)
Arguments
beta |
numeric vector of positive coefficients. |
Value
positive numeric representing the squared L2 norm, or roughness of the kernel with coefficients beta/norm_const(beta).
References
Hofmeyr, D.P. (2021) "Fast exact evaluation of univariate kernel sums", IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(2), 447-458.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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