ucimlr: UCI Machine Learning Repository

#' Abalone
#'
#' Predicting the age of abalone from physical measurements. The age of abalone
#' is determined by cutting the shell through the cone, staining it, and
#' counting the number of rings through a microscope -- a boring and
#' time-consuming task. Other measurements, which are easier to obtain, are
#' used to predict the age. Further information, such as weather patterns and
#' location (hence food availability) may be required to solve the problem.
#'
#' @format A data frame with 4177 observations on the following 9 variables.
#' \enumerate{
#'   \item Sex
#'   \item Length
#'   \item Diameter
#'   \item Height
#'   \item Whole weight
#'   \item Shucked weight
#'   \item Viscera weight
#'   \item Shell weight
#'   \item Rings
#' }
#'
#' @details
#' Predict the age of abalone from physical measurements.
#'
#' @references
#' Warwick J Nash, Tracy L Sellers, Simon R Talbot, Andrew J Cawthorn and Wes B Ford (1994)
#' "The Population Biology of Abalone (_Haliotis_ species) in Tasmania. I. Blacklip Abalone (_H. rubra_) from the North Coast and Islands of Bass Strait",
#' Sea Fisheries Division, Technical Report No. 48 (ISSN 1034-3288)
#'
#' \url{https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/}
#'
#' \url{https://archive.ics.uci.edu/ml/datasets/abalone}
#'
#' @source
#' Marine Resources Division
#' Marine Research Laboratories - Taroona
#' Department of Primary Industry and Fisheries, Tasmania
#' GPO Box 619F, Hobart, Tasmania 7001, Australia
#' (contact: Warwick Nash +61 02 277277, wnash '@' dpi.tas.gov.au)
"abalone"