<commit_before>#' Translate a list of arguments passed to a mungebit into a mungepiece
#'
#' For example, one can pass a training function, a prediction function,
#' and additional arguments, and the constructed mungepiece will hold
#' these arguments, ready to call the attached mungebit on a given dataframe.
#'
#' @param args a list. This can be of the formats
#'   \code{list(train_fn, ...)}, \code{list(list(train_fn, predict_fn), ...)},
#'   and \code{list(list(train_fn, ...), list(predict_fn))}. In the first
#'   example, the train and predict function are assumed to be identical. In
#'   the first two examples, the arguments to these functions are assumed to
#'   be identical (for example, if the same kind of filter needs to be applied
#'   to a data set that is about to be trained as to one about to be
#'   predicted). Finally, the last example is the most flexible and allows
#'   different parameters for the training and prediction function,
#'   respectively. The given training and prediction functions are used to
#'   construct a \code{mungebit}, and the resulting \code{mungebit} and <commit_msg>Update parse_mungepiece.r<commit_after>#' Translate a list of arguments passed to a mungebit into a mungepiece
#'
#' For example, one can pass a training function, a prediction function,
#' and additional arguments, and the constructed mungepiece will hold
#' these arguments, ready to call the attached mungebit on a given dataframe.
#'
#' @param args a list. This can be of the formats
#'   \code{list(train_fn, ...)}, \code{list(list(train_fn, predict_fn), ...)},
#'   and \code{list(list(train_fn, ...), list(predict_fn, ...))}. In the first
#'   example, the train and predict function are assumed to be identical. In
#'   the first two examples, the arguments to these functions are assumed to
#'   be identical (for example, if the same kind of filter needs to be applied
#'   to a data set that is about to be trained as to one about to be
#'   predicted). Finally, the last example is the most flexible and allows
#'   different parameters for the training and prediction function,
#'   respectively. The given training and prediction functions are used to
#'   construct a \code{mungebit}, and the resulting \code{mungebit} and 
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