pipeR: Multi-Paradigm Pipeline Implementation

#' The pipeR package
#'
#' pipeR implements various function chaining methods: \code{\%>>\%} operator,
#' \code{Pipe} object, and \code{pipeline} function. Each represents a distinct
#' pipeline model yet shares a common set of features designed to build
#' easy-to-read/write/maintain pipelines.
#' To learn more, please visit \href{http://renkun.me/pipeR-tutorial}{pipeR Tutorial}.
#' @name pipeR-package
#' @docType package
#' @details
#' pipeR package defines a set of syntax tailored for unified, intuitive piping
#' experience. The package is designed to help organize code as a streamline that
#' is consistent with logic and intuition.
#'
#' The following example shows how traditional code can be written in different
#' function chaining styles.
#' @examples
#' # Traditional code:
#' plot(density(sample(mtcars$mpg, size = 10000, replace = TRUE),
#'  kernel = "gaussian"), col = "red", main="density of mpg (bootstrap)")
#'
#' # Operator-based pipeline using %>>%:
#' mtcars$mpg %>>%
#'   sample(size = 10000, replace = TRUE) %>>%
#'   density(kernel = "gaussian") %>>%
#'   plot(col = "red", main = "density of mpg (bootstrap)")
#'
#' # Object-based pipeline using Pipe():
#' Pipe(mtcars$mpg)$
#'   sample(size = 10000, replace = TRUE)$
#'   density(kernel = "gaussian")$
#'   plot(col = "red", main = "density of mpg (bootstrap)")
#'
#' # Argument-based pipeline using pipeline():
#' pipeline(mtcars$mpg,
#'   sample(size = 10000, replace = TRUE),
#'   density(kernel = "gaussian"),
#'   plot(col = "red", main = "density of mpg (bootstrap)"))
#'
#' # Expression-based pipeline using pipeline():
#' pipeline({
#'   mtcars$mpg
#'   sample(size = 10000, replace = TRUE)
#'   density(kernel = "gaussian")
#'   plot(col = "red", main = "density of mpg (bootstrap)")
#' })
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