1. Calibration and Normalization
                        1. Calibration and Normalization
aroma.light-package     Package aroma.light
averageQuantile.list    Gets the average empirical distribution
backtransformAffine.matrix
                        Reverse affine transformation
backtransformPrincipalCurve.matrix
                        Reverse transformation of principal-curve fit
calibrateMultiscan.matrix
                        Weighted affine calibration of a multiple
                        re-scanned channel
callNaiveGenotypes.numeric
                        Calls genotypes in a normal sample
distanceBetweenLines    Finds the shortest distance between two lines
fitIWPCA.matrix         Robust fit of linear subspace through
                        multidimensional data
fitNaiveGenotypes.numeric
                        Fit naive genotype model from a normal sample
fitPrincipalCurve.matrix
                        Fit a principal curve in K dimensions
fitXYCurve.matrix       Fitting a smooth curve through paired (x,y)
                        data
iwpca.matrix            Fits an R-dimensional hyperplane using
                        iterative re-weighted PCA
likelihood.smooth.spline
                        Calculate the log likelihood of a smoothing
                        spline given the data
medianPolish.matrix     Median polish
normalizeAffine.matrix
                        Weighted affine normalization between channels
                        and arrays
normalizeAverage.matrix
                        Rescales channel vectors to get the same
                        average
normalizeCurveFit.matrix
                        Weighted curve-fit normalization between a pair
                        of channels
normalizeDifferencesToAverage.list
                        Rescales channel vectors to get the same
                        average
normalizeFragmentLength
                        Normalizes signals for PCR fragment-length
                        effects
normalizeQuantileRank.list
                        Normalizes the empirical distribution of a set
                        of samples to a target distribution
normalizeQuantileRank.matrix
                        Weighted sample quantile normalization
normalizeQuantileRank.numeric
                        Normalizes the empirical distribution of a
                        single sample to a target distribution
normalizeQuantileSpline.list
                        Normalizes the empirical distribution of a set
                        of samples to a target distribution
normalizeQuantileSpline.matrix
                        Weighted sample quantile normalization
normalizeQuantileSpline.numeric
                        Normalizes the empirical distribution of a
                        single sample to a target distribution
normalizeTumorBoost.numeric
                        Normalizes allele B fractions for a tumor given
                        a match normal
plotDensity.list        Plots density distributions for a set of
                        vectors
plotMvsA.matrix         Plot log-ratios vs log-intensities
plotMvsAPairs.matrix    Plot log-ratios/log-intensities for all unique
                        pairs of data vectors
plotMvsMPairs.matrix    Plot log-ratios vs log-ratios for all pairs of
                        columns
plotXYCurve.matrix      Plot the relationship between two variables as
                        a smooth curve
plotXYCurve.numeric     Plot the relationship between two variables as
                        a smooth curve
robustSmoothSpline      Robust fit of a Smoothing Spline
sampleCorrelations.matrix
                        Calculates the correlation for random pairs of
                        observations
sampleTuples            Sample tuples of elements from a set
weightedMedian          Weighted Median Value
wpca.matrix             Light-weight Weighted Principal Component
                        Analysis
