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FKSUM (version 0.1.0)

Fast Kernel Sums

Description

Implements the method of Hofmeyr, D.P. (2019) <10.1109/TPAMI.2019.2930501> for fast evaluation of univariate kernel smoothers based on recursive computations. Applications to the basic problems of density and regression function estimation are provided, as well as some projection pursuit methods for which the objective is based on non-parametric functionals of the projected density, or conditional density of a response given projected covariates.

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Version

Install

install.packages('FKSUM')

Monthly Downloads

282

Version

0.1.0

License

GPL

Maintainer

David Hofmeyr

Last Published

December 2nd, 2019

Functions in FKSUM (0.1.0)

cbin_alloc

Allocation of points to bins
fk_ICA

Independent component analysis with sample entropy estimated via kernel density
f_ppr

Projection index for projection pursuit regression
df_ppr

Gradient of the projection index for projection pursuit regression
dksum

Kernel derivative sums
FKSUM-package

Fast Exact Kernel Smoothing
f_ica

Projection index for independent component analysis.
fancy_PPR_initialisation

Initialisation for PPR based on Ridge LM after GAM type smoothing
df_ica

Gradient of projection index for independent component analysis.
bin_wts

Compute discrete bin weights
fk_md_dp

C++ code for evaluating partial gradient of mimimum density hyperplane w.r.t. projected data
fk_md_b

Minimum density hyperplane orthogonal to a vector
fk_md

C++ code for evaluating mimimum density hyperplane from projected data
fk_mdh

Minimum density hyperplanes
fk_fmdh

Projection index for finding minimum density hyperplanes
fk_dfmdh

Gradient of projection index for finding minimum density hyperplanes
fk_NW

Nadaraya-Watson regression estimator
fk_density

Fast univariate kernel density estimation
fk_loc_lin

Local linear regression estimator
fk_is_minim_md

Check if MDH constraints are active
kndksum

Kernel and kernel derivative sums
fk_sum

Fast Exact Kernel Sum Evaluation
norm_const_K

Normalising constant for kernels in FKSUM
h_K_to_Gauss

Bandwidth conversion to Gaussian
norm_K

The L2 norm of a kernel
fk_ppr

Projection pursuit regression with local linear kernel smoother
kLLreg

Leave-one-out regression smoother
fk_regression

Fast univariate kernel regression
h_Gauss_to_K

Bandwidth conversion from Gaussian
whiten

Whitening (standardising) a data matrix
var_K

Variance of a kernel
ksum

Kernel sums
roughness_K

Kernel roughness
sm_bin_wts

Compute smoothed bin weights
plot_kernel

Plot the shape of a kernel function implemented in FKSUM based on its vector of beta coefficients
predict.fk_ppr

Predict method for class fk_ppr