hdrm: High-Dimensional Regression Modeling

#' Multiclass cancer prediction involving 14 tumor types
#' 
#' @description
#' 
#' The optimal treatment of cancer patients depends on establishing accurate
#' diagnoses using clinical and histopathologic data. In some instances,
#' particularly involving metastatic tumors, this is difficult to atypical
#' clinical presentation or histopathology. The purpose of this study was to
#' determine whether the diagnosis of common adult malignancies could be
#' achieved purely by molecular classification.  The data consists of 218 tumor
#' samples, spanning 14 common tumor types.  For each tumor, the expression of
#' 16,063 genes were measured.
#' 
#' In addition to the 144 observations available for training, another 54 observations are available for testing.
#' 
#' @format
#' 
#' * `y`: A 14-level factor indicating the type of tumor
#' 
#' * `X`: A matrix with 144 rows and 16063 columns of gene expression
#'   measurements
#' 
#' * `y.test`: Same as above, but for independent testing
#' 
#' * `X.test`: Same as above, but for independent testing
#' 
#' ### Annotation
#' 
#' * The object `fData` contains the associated gene names and gene symbols for
#'   the (mapped) probes in `X`.  Rows of `fData` correspond to columns of `X`,
#'   and are named accordingly.
#' 
#' @source
#' 
#' I (Patrick Breheny) obtained the raw data from [Trevor Hastie](https://web.stanford.edu/~hastie/glmnet/glmnetData); I am not sure what sort of preprocessing/normalization was done to it.  I added the annotation, both with respect to the tumor types and the feature data.
#' 
#' Original citation:
#' 
#' Ramaswamy S, Tamayo P, Rifkin R, Mukherjee S, Yeang C, Angelo M, Ladd C, Reich M, Latulippe E, Mesirov J and others (2001). Multiclass cancer diagnosis using tumor gene expression signatures. *Proceedings of the National Academy of Sciences*, **98**: 15149-15154.
#' 
#' @name Ramaswamy2001
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