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# Part of the rstap package for estimating model parameters
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 3
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#' Example model
#'
#' A model for use in \pkg{rstap} examples.
#'
#' @name example_model
#' @format Calling \code{example("example_model")} will run the model in the
#' Examples section, below, and the resulting stapreg object will then be
#' available in the global environment. The \code{chains} and \code{iter}
#' arguments are specified to make this example small in size. In practice,
#' we recommend that they be left unspecified in order to use the default
#' values (4 and 2000 respectively) or increased if there are convergence
#' problems. The \code{cores} argument is optional and on a multicore system,
#' the user may well want to set that equal to the number of chains being
#' executed.
#'
#' @seealso The Longituinal \href{https://biostatistics4socialimpact.github.io/rstap/articles/longitudinal-I.html}{Vignette} for \code{stap_glmer}.
#'
#' @examples
#' ## following lines make example run faster
#' distdata <- subset(homog_longitudinal_bef_data[,c("subj_ID","measure_ID","class","dist")],
#' subj_ID<=10)
#' timedata <- subset(homog_longitudinal_bef_data[,c("subj_ID","measure_ID","class","time")],
#' subj_ID<=10)
#' timedata$time <- as.numeric(timedata$time)
#' subjdata <- subset(homog_longitudinal_subject_data,subj_ID<=10)
#' example_model <-
#' stap_glmer(y_bern ~ centered_income + sex + centered_age + stap(Coffee_Shop) + (1|subj_ID),
#' family = gaussian(),
#' subject_data = subjdata,
#' distance_data = distdata,
#' time_data = timedata,
#' subject_ID = 'subj_ID',
#' group_ID = 'measure_ID',
#' prior_intercept = normal(location = 25, scale = 4, autoscale = FALSE),
#' prior = normal(location = 0, scale = 4, autoscale = FALSE),
#' prior_stap = normal(location = 0, scale = 4),
#' prior_theta = list(Coffee_Shop = list(spatial = log_normal(location = 1,
#' scale = 1),
#' temporal = log_normal(location = 1,
#' scale = 1))),
#' max_distance = 3, max_time = 50,
#' # chains, cores, and iter set to make the example small and fast
#' chains = 1, iter = 25, cores = 1)
#'
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