added univariate fable model

NAMESPACE

@@ -1,12 +1,40 @@
 # Generated by roxygen2: do not edit by hand
 
-export(DeterministicModel)
-export(EndogenousSimulator)
-export(EndogenousSystem)
-export(ExogenModel)
-export(LinearRegressionModel)
-export(Model)
-export(StochasticStaticModel)
+S3method(predict,deterministic)
+S3method(predict,exogen)
+S3method(predict,linear)
+S3method(predict,parametric_distribution)
+S3method(predict,univariate_fable)
+export(bootstraplm)
+export(build_model)
+export(create_distribution_object)
+export(create_panel_frame)
+export(deterministicmodel)
+export(dt_ls)
+export(exogenmodel)
+export(fit_parametric_distribution_model)
+export(get_accuracy)
+export(get_execution_order)
+export(get_independent_models)
+export(get_sepi)
+export(getpi)
+export(linearmodel)
+export(new_endogenmodel)
+export(parametric_distribution_model)
+export(parse_formula)
+export(plotsim)
+export(prepare_simulation_data)
+export(process_dependent_models)
+export(process_independent_models)
+export(pt_ls)
+export(qt_ls)
+export(rt_ls)
+export(select_col_per_row)
+export(setup_simulator)
+export(sim_to_dist)
+export(simulate_endogenr)
+export(univariate_fable_model)
+export(update_dependency_graph)
 importFrom(data.table,":=")
 importFrom(data.table,.BY)
 importFrom(data.table,.GRP)

R/systemsim.R

@@ -221,7 +221,7 @@ simulate_endogenr <- function(nsim, simulator_setup, parallel = FALSE, ncores =
                                                       horizon = simulator_setup$horizon,
                                                       execution_order = simulator_setup$execution_order,
                                                       future.seed = TRUE,
-                                                      future.packages = "dplyr")
+                                                      future.packages = c("dplyr", "endogenr"))
     future::plan(old_plan)
   } else{
     simulation_results <- lapply(1:nsim, simulate,

R/univariate_fable_model.R

@@ -0,0 +1,58 @@
+#' Univariate model from fable
+#'
+#' @param formula
+#' @param data
+#' @param ...
+#'
+#' @return
+#' @export
+#'
+#' @examples
+univariate_fable_model <- function(formula = NULL, data = NULL, ...){
+  model <- new_endogenmodel(formula)
+
+  model$fitted <- df |> fabletools::model(fable::ARIMA(formula))
+
+
+  class(model) <- c("univariate_fable", class(model))
+  model$independent <- TRUE
+
+  outcome <- parse_formula(model)$outcome
+  return(model)
+}
+
+#' Predict univariate fable model
+#'
+#' @param model
+#' @param horizon
+#' @param test_start
+#' @param data
+#'
+#' @return
+#' @export
+#'
+#' @examples
+predict.univariate_fable <- function(model, horizon, test_start, data){
+  # Get index and key variables from tsibble
+  idx <- tsibble::index_var(data)
+  grp <- tsibble::key_vars(data)
+
+  forecast <- model$fitted |> fabletools::forecast(h = paste(horizon, "years")) |>
+    dplyr::mutate(sample = distributional::generate(!!rlang::sym(model$outcome), 1) |> unlist()) |>
+    dplyr::select(dplyr::all_of(c(grp, idx, "sample"))) |>
+    tsibble::tsibble(key = grp, index = grp)
+
+  # Create prediction data frame with only necessary columns
+  pred_data <- data |>
+    dplyr::filter(!!rlang::sym(idx) >= test_start) |>
+    dplyr::select(
+      !!!rlang::syms(grp),
+      !!rlang::sym(idx),
+      !!rlang::sym(model$outcome)
+    ) |>
+    tsibble::tsibble(key = grp, index = grp)
+
+  pred_data <- dplyr::left_join(pred_data, forecast)
+
+  return(pred_data)
+}