CALF: Coarse Approximation Linear Function

#' @name CALF-package
#' @aliases CALF-package
#' @title Coarse Approximation Linear Function
#' @description Forward selection linear regression greedy algorithm.
#' @encoding UTF-8
#' @author { Stephanie Lane [aut, cre],\cr
#'    John Ford [aut],\cr
#'    Clark Jeffries [aut],\cr
#'    Diana Perkins [aut]
#' }
#' Maintainer: John Ford \email{JoRuFo@@gmail.com}
#' @importFrom stats t.test cor
#' @importFrom utils write.table
#' @import ggplot2
#' @keywords calf
#' @details The Coarse Approximation Linear Function (CALF) algorithm is a type of forward selection
#' linear regression greedy algorithm. Nonzero weights are restricted to the values +1 and -1 and
#' their number limited by an input parameter.  CALF operates similarly on two different types of samples,
#' binary and nonbinary, with some notable distinctions between the two.
#' All sample data is provided to CALF as a data matrix. A binary sample must contain a distinguished first
#' column with at least one 0 entries (e.g. controls) and at least one 1 entry (e.g. cases); at least one
#' other column contains predictor values of some type.  A nonbinary sample is similar but must contain a 
#' first column with real dependent (target) values. Columns containing values other that 0 or 1 must be 
#' normalized, e.g. as z-scores.
#' As its score of differentiation, CALF uses either the Welch t-statistic p-value or AUC for binary samples
#' and the Pearson correlation for non-binary samples, selected by input parameter.  When initiated CALF
#' selects from all predictors (markers) (first in the case of a tie) the one that yields the best score.
#' CALF then checks if the number of selected markers is equal to the limit provided and terminates if so.
#' Otherwise, CALF seeks a second marker, if any, that best improves the score of the sum function generated
#' by adding the newly selected marker to the previous markers with weight +1 or weight -1.
#' The process continues until the limit is reached or until no additional marker can be included in the sum
#' to improve the score.
#' By default, for binary samples, CALF assumes control data is designated with a 0 and case data with a 1.
#' It is allowable to use the opposite convention, however the weights in the final sum may need to be reversed.
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