hddplot: Use Known Groups in High-Dimensional Data to Derive Scores for Plots

Cross-validated linear discriminant calculations determine the optimum number of features. Test and training scores from successive cross-validation steps determine, via a principal components calculation, a low-dimensional global space onto which test scores are projected, in order to plot them. Further functions are included that are intended for didactic use. The package implements, and extends, methods described in J.H. Maindonald and C.J. Burden (2005) <https://journal.austms.org.au/V46/CTAC2004/Main/home.html>.

Version:	0.59
Depends:	R (≥ 3.0.0)
Imports:	MASS, multtest
Suggests:	knitr
Published:	2018-06-15
Author:	John Maindonald
Maintainer:	John Maindonald <jhmaindonald at gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	http://maths-people.anu.edu.au/~johnm/
NeedsCompilation:	no
Citation:	hddplot citation info
Materials:	README
CRAN checks:	hddplot results

Documentation:

Reference manual:	hddplot.pdf
Vignettes:	Feature Selection Bias in Classification of High Dimensional Data

Downloads:

Package source:	hddplot_0.59.tar.gz
Windows binaries:	r-devel: hddplot_0.59.zip, r-release: hddplot_0.59.zip, r-oldrel: hddplot_0.59.zip
macOS binaries:	r-release (arm64): hddplot_0.59.tgz, r-oldrel (arm64): hddplot_0.59.tgz, r-release (x86_64): hddplot_0.59.tgz, r-oldrel (x86_64): hddplot_0.59.tgz
Old sources:	hddplot archive

Linking:

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