Normalization based a subset of negative control probes as
        described in 'Subset quantile normalization using negative
        control features'. Wu Z, Aryee MJ, J Comput Biol. 2010
        Oct;17(10):1385-95 [PMID 20976876].

Provides simple and powerful interfaces that facilitate interaction
    with 'ODBC' data sources. Each data source gets its own unique and dedicated
    interface, wrapped around 'RODBC'. Communication settings are remembered
    between queries, and are managed silently in the background. The interfaces
    support multi-statement 'SQL' scripts, which can be parameterised via
    metaprogramming structures and embedded 'R' expressions.

A subgroup identification method for precision medicine based on 
    quantitative objectives. This method can handle continuous, binary and 
    survival endpoint for both prognostic and predictive case. For the 
    predictive case, the method aims at identifying a subgroup for which 
    treatment is better than control by at least a pre-specified or 
    auto-selected constant. For the prognostic case, the method aims at 
    identifying a subgroup that is at least better than a 
    pre-specified/auto-selected constant. The derived signature is a linear 
    combination of predictors, and the selected subgroup are subjects with 
    the signature > 0. The false discover rate when no true subgroup exists
    is controlled at a user-specified level.

Algorithms for accelerating the convergence of slow,
        monotone sequences from smooth, contraction mapping such as the
        EM algorithm. It can be used to accelerate any smooth, linearly
        convergent acceleration scheme.  A tutorial style introduction
        to this package is available in a vignette on the CRAN download
        page or, when the package is loaded in an R session, with
        vignette("SQUAREM"). Refer to the J Stat Software article: <doi:10.18637/jss.v092.i07>.

Functions for color-based visualization of multivariate data, i.e. colorgrams or heatmaps.  Lower-level functions  map numeric values to colors, display a matrix as an array of colors, and draw color keys.  Higher-level plotting functions generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more.