Threshing and Reaping for Principal Components

Defines the classes used to identify outliers (threshing) and compute the number of significant principal components and number of clusters (reaping) in a joint application of PCA and hierarchical clustering. See Wang et al., 2018, .


News

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	CHANGES IN R VERSION 1.1.0

NEW FEATURES

o	Added a "remap" function to match numeric labels across
	different cluster assignments.

	CHANGES IN R VERSION 1.0.2

BUG FIXES

o	Simulation code works differently on different machines, even
    when setting the same seed. The issue can be traced through
    reliance on 'mvrnorm"' and 'eigen' to simulate multivariate
    normal data. When there are repeated eigenvalues, different
    linear alg=ebra routines can (legitimetaly) choose different
    orthogonal bases for the correrspsoninf multidimensional
    eigenspace. Have tried to refactor the test code so that this
    doesn't matter.

MAINTENANCE

o	Updated use of S4 class defintions to avod newly deprecated methods.

	CHANGES IN R VERSION 1.0.0

NEW FEATURES

o	Initial version. Classes Thresher, Reaper, Auer-Gervini.

BUG FIXES

o	Numerous.

Reference manual

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install.packages("Thresher")

1.1.0 by Kevin R. Coombes, 16 days ago


http://oompa.r-forge.r-project.org/


Browse source code at https://github.com/cran/Thresher


Authors: Kevin R. Coombes


Documentation:   PDF Manual  


Apache License (== 2.0) license


Imports methods, stats, graphics, grDevices, MASS, colorspace, movMF, ade4, oompaBase

Depends on ClassDiscovery, PCDimension

Suggests NbClust


See at CRAN