edd Expression density diagnostics: graphical methods and pattern recognition algorithms for distribution shape classification.
factDesign Provides a set of tools for analyzing data from factorial designed microarray experiments. The functions can be used to evaluate appropriate tests of contrast and perform single outlier detection
genefilter Tools for sequentially filtering genes using a wide variety of filtering functions. Example of filters include: number of missing value, coefficient of variation of expression measures, ANOVA p-value, Cox model p-values. Sequential application of filtering functions to genes
globaltest Testing globally whether a group of genes is significantly relatedto some clinical variable of interest.
gpls Classification using generalized partial least squares for two-group and multi-group
limma Linear models for microarray data
RMAGEML Used to handle MAGE-ML documents in Bioconductor
MeasurementError.cor Two-stage measurement error model for correlation estimation with smaller bias then the usual sample correlation
multtest Multiple testing procedures for controlling the family-wise error rate (FWER) and the false discovery rate (FDR). Tests can be based on t- or F-statistics for one- and two-factor designs, and permutation procedures are available to estimate adjusted p-values.
pamr Some functions for sample classification in microarrays
ROC Receiver Operating Characteristic (ROC) approach for identifying genes that are differentially expressed int two types of samples.
siggenes Identifying differentially expressed genes and estimating the False Discovery Rate with both the Significance Analysis of Microarrays and the Empirical Bayes Analyses of Microarrays
splicegear A set of tools to work with alternative splicing