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supclust

supclust is an R package which has been on CRAN since January 2004, see (https://cran.r-project.org/package=supclust).

It contains C and R code, written by Marcel Dettling in 2003, implementing the WILMA and PELORA algorithms for supervised clustering of potentially many predictor variables, notably for gene modeling.

Martin Maechler who had helped originally in getting the R package to CRAN had taken maintenance in 2009, to prevent long term orphaning of the package.

The source code has been moved to github only on Oct 15, 2020, using my own G2RCSn utility and notably the rcs-fast-export.rb ruby script, see also my blog post about how I achieved such a transformation for my sfsmisc CRAN R package.

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Install

install.packages('supclust')

Monthly Downloads

242

Version

1.1-1

License

GPL-3

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Last Published

September 27th, 2021

Functions in supclust (1.1-1)

score

Wilcoxon Score for Binary Problems
summary.wilma

Summary Method for Wilma Objects
wilma

Supervised Clustering of Predictor Variables
summary.pelora

Summary Method for Pelora Objects
print.wilma

Print Method for Wilma Objects
standardize.genes

Standardization of Predictor Variables
predict.pelora

Predict Method for Pelora
sign.change

Sign-flipping of Predictor Variables to Obtain Equal Polarity
predict.wilma

Predict Method for Wilma
sign.flip

Sign-flipping of Predictor Variables to Obtain Equal Polarity
print.pelora

Print Method for Pelora Objects
pelora

Supervised Grouping of Predictor Variables
fitted.pelora

Extract the Fitted Values of Pelora
back.search

Internal functions for Supervised Grouping
fitted.wilma

Extract the Fitted Values of Wilma
plot.pelora

2-Dimensional Visualization of Pelora's Output
dlda

Classification with Wilma's Clusters
leukemia

A part of the Golub's famous AML/ALL-leukemia dataset
margin

Classification Margin Between Two Sample Classes
coef.pelora

Extract the Model Coefficients of Pelora
plot.wilma

2-Dimensional Visualization of Wilma's Output