A novel implementation that solves the linear distance weighted discrimination and the kernel distance weighted discrimination.

Boxiang Wang <boxiang-wang@uiowa.edu>, Hui Zou <hzou@stat.umn.edu>
A novel implementation that solves the linear distance weighted discrimination and the kernel distance weighted discrimination. Reference: Wang and Zou (2018) <doi:10.1111/rssb.12244>.

Kernel Factory: An ensemble of kernel Machines
Classification based on an ensemble of kernel machines

Kernel Factory: An Ensemble of Kernel Machines
Binary classification based on an ensemble of kernel machines ("Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), 2904-2913"). Kernel factory is an ensemble method where each base classifier (random forest) is fit on the kernel matrix of a subset of the training data.

randomForest, AUC, genalg, kernlab, stats

Post-Selection Inference for Nonlinear Variable Selection
Different post-selection inference strategies for kernel
selection, as described in "kernelPSI: a Post-Selection Inference Framework
for Nonlinear Variable Selection", Slim et al., Proceedings of Machine
Learning Research, 2019, <http://proceedings.mlr.press/v97/slim19a/slim19a.pdf>. The strategies rest upon quadratic kernel
association scores to measure the association between a given kernel and an
outcome of interest. The inference step tests for the joint effect of the
selected kernels on the outcome. A fast constrained sampling algorithm is
proposed to derive empirical p-values for the test statistics.
Rcpp (>= 1.0.1), CompQuadForm, tmg, pracma, kernlab, lmtest
bindata, knitr, rmarkdown, MASS, testthat

Rcpp (>= 1.0.1), CompQuadForm, pracma, kernlab, lmtest

Kernel Smoothing Tools for Philology and Historical Dialectology
person(given = "Tamsin",
family = "Blaxter",
role = c("aut", "cre"),
email = "ttb26@cam.ac.uk",
comment = c(ORCID = "0000-0002-1466-8306"))
Contains kernel smoothing tools designed for use by historical
dialectologists and philologists for exploring spatial and temporal patterns
in noisy historical language data, such as that obtained from historical texts.
The main way in which these might differ from other implementations of kernel
smoothing is that they assume that the function (linguistic variable) being
explored has the form of the relative frequency of a series of discrete
possibilities (linguistic variants). This package also offers a way of exploring
distributions in 2-dimensional space and in time with separate kernels, and
tools for identifying appropriate bandwidths for these.
benchmarkme, directlabels, dplyr, ggplot2, grDevices,
gridExtra, Hmisc, pbapply, rgdal, reshape2, stats, utils,
wordspace
Tamsin Blaxter [aut, cre] (<https://orcid.org/0000-0002-1466-8306>)