R/data.R

In DynCount: Bayesian Dynamic Models for Count Time Series

# Example data documentation --------------------------------------------------

#' Weekly English Channel crossings (UK example)
#'
#' A weekly count series of irregular small-boat crossings of the English
#' Channel towards the United Kingdom, covering the ISO weeks 2018-W01 to
#' 2025-W11 (376 weeks). The series has frequent zeros in its early weeks
#' (43% of the first 130 weeks), which makes it a useful example for zero
#' inflation with the Poisson model. The series is used in the
#' irregular-migration application of Zens and Bijak (2026).
#'
#' @format A data frame with 376 rows and 3 variables:
#' \describe{
#'   \item{week}{ISO week label, e.g. `"2018-W01"`.}
#'   \item{count}{Non-negative integer count of weekly crossings.}
#'   \item{date}{The Monday of the ISO week, as a `Date`.}
#' }
#' @source Weekly aggregates of detected irregular English Channel crossings,
#'   compiled from operational/agency records as described in Zens and Bijak
#'   (2026), \doi{10.1214/26-AOAS2171}.
#' @references
#' Zens, G. and Bijak, J. (2026). Dynamic Count Models with Flexible Innovation
#' Processes for Irregular Maritime Migration. \emph{The Annals of Applied
#' Statistics}, 20(2), 1671--1690. \doi{10.1214/26-AOAS2171}.
#' @examples
#' plot(uk_weekly$date, uk_weekly$count, type = "h", xlab = "week", ylab = "crossings")
#' mean(uk_weekly$count == 0)
"uk_weekly"

#' Weekly Mediterranean crossings (Mediterranean example)
#'
#' A longer weekly count series of irregular sea crossings on the Mediterranean
#' route, covering the ISO weeks 2015-W40 to 2025-W11 (494 weeks). The counts
#' are larger in magnitude with fewer zeros than [uk_weekly]. The series is
#' used in the irregular-migration application of Zens and Bijak (2026).
#'
#' @format A data frame with 494 rows and 3 variables:
#' \describe{
#'   \item{week}{ISO week label, e.g. `"2015-W40"`.}
#'   \item{count}{Non-negative integer count of weekly crossings.}
#'   \item{date}{The Monday of the ISO week, as a `Date`.}
#' }
#' @source Weekly aggregates of detected irregular Mediterranean crossings,
#'   compiled from operational/agency records as described in Zens and Bijak
#'   (2026), \doi{10.1214/26-AOAS2171}.
#' @references
#' Zens, G. and Bijak, J. (2026). Dynamic Count Models with Flexible Innovation
#' Processes for Irregular Maritime Migration. \emph{The Annals of Applied
#' Statistics}, 20(2), 1671--1690. \doi{10.1214/26-AOAS2171}.
#' @examples
#' summary(med_weekly$count)
#' plot(med_weekly$date, med_weekly$count, type = "h", xlab = "week", ylab = "crossings")
"med_weekly"