#' Dataset for Signal Detection Analysis, reported cases, 1999-2018 (ECDC Atlas export)
#'
#' A dataset containing an export from the ECDC Atlas for salmonellosis and measles data.
#' This export is cleaned and ready for Signal Detection Analysis (see. cleanAtlasExport() )
#'
#' @format A data frame with 80,834 rows and 11 variables:
#' \describe{
#' \item{HealthTopic}{Disease name e.g. Salmonellosis or Measles}
#' \item{Population}{Population characteristics e.g. All cases, Confirmed cases, Serotype AGONA,
#' Serotype BAREILLY etc.}
#' \item{Indicator}{Indicator e.g. Hospitalised cases, Reported cases, Number of deaths, etc.}
#' \item{Time}{Time variable including both yearly data from 1999 to 2017, and monthly data from 1999-01 to 2018-02}
#' \item{RegionName}{Geographical level including country names e.g. Austria, Belgium, Bulgaria, etc.}
#' \item{NumValue}{Number of cases}
#' \item{TimeUnit}{Time unit corresponding to the format of the date in the 'Time' variable e.g. Year or Month}
#' \item{TimeYear}{Year of the date available in the 'Time' variable, regardless of the date format
#' i.e. 1999 to 2018}
#' \item{TimeMonth}{Month of the date available in the 'Time' variable, regardless of the date format i.e. 1 to 12}
#' \item{TimeWeek}{Week of the date available in the 'Time' variable, regardless of the date format
#' i.e. NA since this dataset does not include any weekly data}
#' \item{TimeDate}{Approximated date corresponding to the date available in the 'Time' variable (daily format)}
#' }
#' @docType data
#' @keywords datasets
#' @name SignalData
#' @usage SignalData
#' @source \url{http://atlas.ecdc.europa.eu/public/index.aspx}
"SignalData"
#' List of datasets containing the Farrington Flexible and GLRNB default parameters by time unit
#'
#' A list including two datasets containing the parameters used for Farrington Flexible and for GLRNB
#' for each time unit available in the Signal Detection tool
#'
#' @seealso \code{surveillance::farringtonFlexible} \code{surveillance::glrnb}
#'
#' @format A list of 2 dataframes: one with 2 rows and 9 variables and GRLNB with 2 rows and 8 variables
#' \enumerate{
#' \item \strong{Default parameters for FarringtonFlexible algorithm}
#' \describe{
#' \item{timeunit}{Time units available in the signal detection tool i.e. week, month}
#' \item{w}{Window's half-size, i.e. number of weeks to include before and after the current week in each year
#' (w=2 for weeks, w=1 for months)}
#' \item{reweight}{Logical specifying whether to reweight past outbreaks or not
#' (TRUE for both weeks and months, past outbreaks are always reweighted)}
#' \item{trend}{Logical specifying whether a trend should be included and kept
#' in case the conditions in the Farrington et. al. paper are met.
#' (TRUE for both weeks and months, a trend is always fit)}
#' \item{weightsThreshold}{Numeric defining the threshold for reweighting past outbreaks
#' using the Anscombe residuals
#' (2.85 for both weeks and months, as advised in the improved method)}
#' \item{glmWarnings}{Logical specifying whether to print warnings from the call to glm
#' (TRUE for both weeks and months)}
#' \item{pThresholdTrend}{Numeric defining the threshold for deciding whether to keep trend in the model
#' (0.05 for both weeks and months)}
#' \item{limit54_1}{Integer, the number of cases defining a threshold for minimum alarm,
#' no alarm is sounded if fewer than 'limit54_1' cases were reported in the past 'limit54_2' weeks/months}
#' \item{limit54_2}{Integer, the number of periods defining a threshold for minimum alarm,
#' no alarm is sounded if fewer than 'limit54_1' cases were reported in the past 'limit54_2' weeks/months}
#' }
#'
#'
#' \item \strong{Default parameters for GLRNB algorithm}
#' \describe{
#' \item{timeunit}{Time units available in the signal detection tool i.e. week, month}
#' \item{mu0}{A vector of in-control values of the mean of the Poisson / negative binomial distribution
#' with the same length as range
#' - NULL for both weeks and months}
#' \item{theta}{Numeric, the pre-specified value for k or lambda is used in a recursive LR scheme
#' - log(1.2) for both weeks and months corresponding to a 20 percent increase in the mean}
#' \item{alpha}{Numeric, the dispersion parameter of the negative binomial distribution.
#' If alpha=NULL the parameter is calculated as part of the in-control estimation
#' - alpha=NULL for both weeks and months}
#' \item{cARL}{Numeric, the threshold in the GLR test, i.e. c_gamma - cARL=0.25 for both weeks and months}
#' \item{Mtilde}{Integer, the number of observations needed before we have a full rank
#' - Mtilde=1 for both weeks and months}
#' \item{M}{Integer defining the number of time instances back in time in the window-limited approach.
#' To always look back until the first observation use M=-1. M=1 for both weeks and months}
#' \item{Change}{Character string specifying the type of the alternative.
#' Currently the two choices are intercept and epi
#' - Change=intercept for both weeks and months}
#' }
#'
#' }
#' @docType data
#' @keywords datasets
#' @name AlgoParam
#' @usage AlgoParam
"AlgoParam"
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