FKSUM v0.1.0
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Fast Kernel Sums
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.
Functions in FKSUM
| Name | Description | |
| 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 | |
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Details
| Type | Package |
| License | GPL |
| Encoding | UTF-8 |
| LinkingTo | Rcpp, RcppArmadillo |
| LazyData | true |
| NeedsCompilation | yes |
| Packaged | 2019-11-29 14:59:43 UTC; david |
| Repository | CRAN |
| Date/Publication | 2019-12-02 16:40:06 UTC |
| imports | MASS , rARPACK |
| depends | Rcpp (>= 0.12.16) |
| linkingto | RcppArmadillo |
| Contributors | David Hofmeyr |
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