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#' Model specification for a GAM for SDM
#'
#' This function returns a [parsnip::model_spec] for a General Additive Model to
#' be used as a classifier of presences and absences in Species Distribution
#' Model.
#'
#' Note that, when using GAMs in a `workflow_set()`, it is necessary to update
#' the model with [gam_formula()] (see [`parsnip::model_formula`] for a
#' discussion of formulas with special terms in `tidymodels`):
#' \preformatted{
#' workflow_set(
#' preproc = list(default = my_recipe),
#' models = list(gam = sdm_spec_gam()),
#' cross = TRUE
#' ) \%>\% update_workflow_model("default_gam",
#' spec = sdm_spec_gam(),
#' formula = gam_formula(my_recipe))
#' }
#' @param ... parameters to be passed to [parsnip::gen_additive_mod()] to
#' customise the model. See the help of that function for details.
#' @param tune character defining the tuning strategy. As there are no
#' hyperparameters to tune in a *gam*, the only valid option is "none". This
#' parameter is present for consistency with other `sdm_spec_*` functions, but
#' it does nothing in this case.
#' @returns a [parsnip::model_spec] of the model.
#' @examples
#' my_gam_spec <- sdm_spec_gam()
#' @family "sdm model specifications"
#' @export
#' @seealso [parsnip::gen_additive_mod()] [gam_formula()]
sdm_spec_gam <- function(..., tune = "none") {
tune <- rlang::arg_match(tune)
parsnip::gen_additive_mod(...) %>% # model type
parsnip::set_engine(engine = "mgcv") %>% # model engine
parsnip::set_mode("classification") # model mode
}
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