R (>= 2.10), class, e1071, gclus, lars, MASS, mclust, nnet,
pcaPP, pls, rpart, robustbase, som

This package is the R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009)

class, e1071, MASS, nnet, pcaPP, robustbase, som, lars, pls,
mclust

R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009).

A tool for the design of synthetic experiments in machine
olfaction.
Alexandre Perera-Lluna and Andrey Ziyatdinov
This package introduces a software tool for the design of
synthetic experiments in machine olfaction. The generator of gas sensor
signals can be used in applications related to test and benchmarking of
signal processing methods, neuromorphic simulations in machine olfaction
and educational tools. A so-called virtual sensor array features unlimited
number of sensors, support of multicomponent gas mixtures and full
parametric control of the noise in the system. The artificial (virtual)
sensor array is inspired from a reference database of seventeen polymeric
sensors with concentration profiles for three analytes.
R (>= 2.11.0), methods, stats, pls, plyr, reshape2, ggplot2
'package.R' 'datasets.R' 'methods.R' 'demos.R' 'dev.lib.R'
'support.lib.R' 'spline.lib.R' 'ChemosensorsClass.R'
'ChemosensorsClassMethods.R' 'conc.lib.R' 'sdata.lib.R'
'plot.lib.R' 'SensorDataModelClassMethods.R'
'ConcNoiseModelClass.R' 'ConcNoiseModelClassMethods.R'
'DriftCommonModelClassMethods.R' 'DriftNoiseModelClass.R'
'DriftNoiseModelClassMethods.R' 'ScenarioClass.R'
'ScenarioClassMethods.R' 'SensorDynamicsClass.R'
'SensorDynamicsClassMethods.R' 'SensorModelClass.R'
'SensorModelClassMethods.R' 'SensorNoiseModelClass.R'
'SensorNoiseModelClassMethods.R' 'SorptionModelClass.R'
'SorptionModelClassMethods.R' 'SensorArrayClass.R'
'SensorArrayClassMethods.R' 'affinity.lib.R'

Multiple testing methods for exploratory research
This package implements a class of multiple testing
methods that does not prescribe the user what hypotheses to
reject, but instead allows the user to select the set of
selected hypotheses freely. The multiple testing procedure aids
this selection by providing an upper confidence bound for the
number of false rejections among the selected set. Because the
confidence bounds are simultaneous for all possible rejected
sets, the validity is not compromised by post hoc selection.