version 1.0.x; i.e. the Kooperberg and Stone (1992)

Authors@R: c(person("Charles", "Kooperberg", role = c("aut", "cre"),

Description: Routines for the logspline density estimation.
oldlogspline uses the same algorithm as the logspline 1.0.x
package - the Kooperberg and Stone (1992) algorithm (with an
improved interface). The recomended routine logspline uses an

improved interface). The recommended routine logspline uses an

Title: Logspline Density Estimation Routines
and Stone (1992) algorithm (with an improved interface). The recommended

Description: Routines for the logspline density estimation. oldlogspline()
and Stone (1992) <DOI: 10.2307/1390786> algorithm (with an improved interface).
The recommended routine logspline() uses an algorithm from Stone et al (1997).

Title: Kernel Regression Smoothing with Local or Global Plug-in Bandwidth
Author: Eva Herrmann <eherrmann@mathematik.tu-darmstadt.de> (F77 & S original);
Description: Kernel regression smoothing with adaptive local or global plug-in

Title: Kernel Regression Smoothing with Local or Global Plug-in
Author: Eva Herrmann <eherrmann@mathematik.tu-darmstadt.de> (F77 & S
original); Packaged for R and enhanced by Martin Maechler
Description: Kernel regression smoothing with adaptive local or global

Packaged for R and enhanced by Martin Maechler

Imports: grDevices, graphics, stats, utils, sfsmisc (>= 1.0-12)

Title: Linear Optimal Low-Rank Projection (LOL)
Authors@R: c(person("Eric", "Bridgeford", role = c("aut", "cre"),
person("Joshua", "Vogelstein", role = "ths",
Description: Supervised learning techniques designed for the situation when the dimensionality exceeds the sample size have a tendency to overfit as the dimensionality of the data increases. To remedy this High dimensionality; low sample size (HDLSS) situation, we attempt to learn a lower-dimensional representation of the data before learning a classifier. That is, we project the data to a situation where the dimensionality is more manageable, and then are able to better apply standard classification or clustering techniques since we will have fewer dimensions to overfit. A number of previous works have focused on how to strategically reduce dimensionality in the unsupervised case, yet in the supervised HDLSS regime, few works have attempted to devise dimensionality reduction techniques that leverage the labels associated with the data. In this package, we provide several methods for feature extraction, some utilizing labels and some not, along with easily extensible utilities to simplify cross-validative efforts to identify the best feature extraction method. Additionally, we include a series of adaptable benchmark simulations to serve as a standard for future investigative efforts into supervised HDLSS. Finally, we produce a comprehensive comparison of the included algorithms across a range of benchmark simulations and real data applications.
Suggests: knitr, rmarkdown, parallel, randomForest, latex2exp