R/distantia.R

In BlasBenito/distantia: Assessing Dissimilarity Between Multivariate Time Series

#' @details Details
"_PACKAGE"

#' Pollen dataset.
#'
#' A subset of the Grande Pile dataset (\url{https://doi.pangaea.de/10.1594/PANGAEA.739275}). It contains a depth (cm) and age columns (ky BP), and 40 pollen types.
#'
#'
#' @docType data
#' @keywords datasets
#' @name pollenGP
#' @usage data(sequenceA)
#' @format Dataframe with 42 columns and 200 rows
"pollenGP"


#' Multivariate and irregular time series with pollen counts.
#'
#' A dataframe with 9 columns representing pollen types (betula, pinus, corylus, empetrum, cypera, artemisia, rumex) and 49 rows representing increasing depths with pollen counts taken from the Abernethy dataset (Birks and Mathewes (1978).
#'
#'
#' @docType data
#' @keywords datasets
#' @name sequenceA
#' @usage data(sequenceA)
#' @references Birks, H.H. and Mathewes, R.W. (1978) Studies in the vegetational history of Scotland. \emph{New Phytologist} \strong{80}, 455-484.
#' @format Dataframe with 9 columns and 49 rows
"sequenceA"

#' Multivariate and irregular time series with pollen counts.
#'
#' A dataframe with 8 columns (the column \code{empetr} is missing with respect to \code{\link{sequenceA}}) representing pollen types (betula, pinus, corylus, cypera, artemisia, rumex) and 41 rows representing increasing depths with pollen counts taken from the Abernethy dataset (Birks and Mathewes (1978). Several NA values have been introduced in the dataset to demonstrate the data-handling capabilities of \code{\link{prepareSequences}}.
#'
#'
#' @docType data
#' @keywords datasets
#' @name sequenceB
#' @usage data(sequenceB)
#' @references Birks, H.H. and Mathewes, R.W. (1978) Studies in the vegetational history of Scotland. \emph{New Phytologist} \strong{80}, 455-484.
#' @format Dataframe with 9 columns and 41 rows
"sequenceB"

#' Dataframe with palaeoclimatic data.
#'
#' A dataframe containing 800 simulated samples of palaeoclimate data at 1 ky temporal resolution with the following columns:
#'
#' \itemize{
#'   \item \emph{age} in kiloyears before present (ky BP).
#'   \item \emph{temperatureAverage} average annual temperature in Celsius degrees.
#'   \item \emph{rainfallAverage} average annual precipitation in milimetres per day (mm/day).
#'   \item \emph{temperatureWarmestMonth} average temperature of the warmest month, in Celsius degrees.
#'   \item \emph{temperatureColdestMonth} average temperature of the coldest month, in Celsius degrees.
#'   \item \emph{oxigenIsotope} delta O18, global ratio of stable isotopes in the sea floor, see \url{http://lorraine-lisiecki.com/stack.html} for further details.
#' }
#' @docType data
#' @keywords datasets
#' @name climateLong
#' @usage data(climateLong)
#' @format dataframe with 6 columns and 800 rows.
"climateLong"


#' Dataframe with palaeoclimatic data.
#'
#' A dataframe containing 11 simulated samples of palaeoclimate data at 1 ky temporal resolution with the following columns:
#'
#' \itemize{
#'   \item \emph{temperatureAverage} average annual temperature in Celsius degrees.
#'   \item \emph{rainfallAverage} average annual precipitation in milimetres per day (mm/day).
#'   \item \emph{temperatureWarmestMonth} average temperature of the warmest month, in Celsius degrees.
#'   \item \emph{temperatureColdestMonth} average temperature of the coldest month, in Celsius degrees.
#'   \item \emph{oxigenIsotope} delta O18, global ratio of stable isotopes in the sea floor, see \url{http://lorraine-lisiecki.com/stack.html} for further details.
#' }
#' @docType data
#' @keywords datasets
#' @name climateShort
#' @usage data(climateShort)
#' @format dataframe with 5 columns and 11 rows.
"climateShort"

#' Dataframe with pollen counts for different MIS stages.
#'
#' A dataframe with 427 rows representing pollen counts for 12 marine isotope stages and 6 pollen types
#'
#' @docType data
#' @keywords datasets
#' @name sequencesMIS
#' @usage data(sequencesMIS)
#' @format dataframe with 7 columns and 427 rows.
"sequencesMIS"

#' Dataframe with palaeoclimatic data.
#'
#' A dataframe containing palaeoclimate data at 1 ky temporal resolution with the following columns:
#'
#' \itemize{
#'   \item \emph{time} in kiloyears before present (ky BP).
#'   \item \emph{sequenceId} numeric identifier of sequences of 200ky within the main sequence, useful to test some functions of the package, such as \code{\link{distancePairedSamples}}
#'   \item \emph{temperatureAverage} average annual temperature in Celsius degrees.
#'   \item \emph{rainfallAverage} average annual precipitation in milimetres per day (mm/day).
#'   \item \emph{temperatureWarmestMonth} average temperature of the warmest month, in Celsius degrees.
#'   \item \emph{temperatureColdestMonth} average temperature of the coldest month, in Celsius degrees.
#' }
#' @author Blas M. Benito  <blasbenito@gmail.com>
#' @docType data
#' @keywords datasets
#' @name climate
#' @usage data(climate)
#' @format dataframe with 6 columns and 800 rows.
"climate"

#' @import plyr parallel doParallel foreach iterators fields grDevices viridis data.table arrangements
NULL

#' @import utils
utils::globalVariables(c("i", "..numeric.cols"))