c(person("Martin","Maechler", role=c("aut","cre"), email="maechler@stat.math.ethz.ch")
 ,person("Peter", "Rousseeuw", role="aut", email="peter.rousseeuw@kuleuven.be",
         comment = "Fortran original")
 ,person("Anja", "Struyf", role="aut", comment= "S original")
 ,person("Mia", "Hubert", role="aut", email= "Mia.Hubert@uia.ua.ac.be", comment= "S original")
 ,person("Kurt", "Hornik", role=c("trl", "ctb"),
 	email="Kurt.Hornik@R-project.org", comment="port to R; maintenance(1999-2000)")
 ,person("Matthias", "Studer", role="ctb")
 ,person("Pierre", "Roudier", role="ctb")
 ,person("Juan",   "Gonzalez", role="ctb")
 ,person("Kamil",  "Kozlowski", role="ctb")
 )

two examples using MASS' cov.rob() and mvrnorm(); Matrix
tools for testing

Martin Maechler [aut, cre],
  Peter Rousseeuw [aut] (Fortran original),
  Anja Struyf [aut] (S original),
  Mia Hubert [aut] (S original),
  Kurt Hornik [trl, ctb] (port to R; maintenance(1999-2000)),
  Matthias Studer [ctb],
  Pierre Roudier [ctb],
  Juan Gonzalez [ctb],
  Kamil Kozlowski [ctb]

Variance stabilization and calibration for microarray data

Wolfgang Huber, with contributions from Anja von
        Heydebreck. Many comments and suggestions by users are
        acknowledged, among them Dennis Kostka, David Kreil, Hans-Ulrich
        Klein, Robert Gentleman, Deepayan Sarkar and Gordon Smyth

affydata, hgu95av2cdf, BiocStyle, knitr, dplyr, testthat

The package implements a method for normalising microarray intensities, and works
        for single- and multiple-color arrays. It can also be used
	for data from other technologies, as long as they have similar format. The method uses a
	robust variant of the maximum-likelihood estimator for an additive-multiplicative error
	model and affine calibration. The model incorporates data calibration step (a.k.a.
	normalization), a model for the dependence of the variance on the mean intensity and a
        variance stabilizing data transformation. Differences between
        transformed intensities are analogous to "normalized
        log-ratios". However, in contrast to the latter, their
        variance is independent of the mean, and they are usually more
        sensitive and specific in detecting differential
        transcription.