correct misspelling of NNTAR

Author: matthewphamilton

DESCRIPTION

@@ -11,7 +11,7 @@ Description: Tools for undertaking time series analysis of mental health
     the serious package has been made available as part of the process of
     testing and documenting the package.  If you have any questions,
     please contact the authors (matthew.hamilton1@monash.edu).
-License: GPL-3 + file LICENSE
+License: GPL-3
 URL: https://ready4-dev.github.io/serious/,
     https://github.com/ready4-dev/serious,
     https://ready4-dev.github.io/ready4/
@@ -21,6 +21,7 @@ RoxygenNote: 7.3.2
 Suggests: 
     knitr,
     knitrBootstrap,
+    pkgload,
     rmarkdown,
     testthat
 VignetteBuilder: knitr

R/fn_make.R

@@ -1548,7 +1548,7 @@ make_training_ds <- function (data_tsb, index_1L_chr = "Date", test_1L_int = int
 #' @param key_totals_ls Key totals (a list), Default: NULL
 #' @param metrics_chr Metrics (a character vector), Default: make_metric_vars()
 #' @param models_chr Models (a character vector), Default: c("Mean", "Naïve", "Seasonal naïve", "Drift", "Trend", "LMTS", 
-#'    "ETS", "ARIMA", "NNTEAR", "Prophet", "Reg_ARIMA", "Reg_Prophet", 
+#'    "ETS", "ARIMA", "NENTAR", "Prophet", "Reg_ARIMA", "Reg_Prophet", 
 #'    "Reg_TSLM")
 #' @param model_type_1L_chr Model type (a character vector of length one), Default: 'multiplicative'
 #' @param order_1L_int Order (an integer vector of length one), Default: 2
@@ -1575,7 +1575,7 @@ make_ts_models <- function (data_xx, approximation_xx = NULL, collapse_1L_lgl =
         "yearly"), index_1L_chr = character(0), join_to_args_ls = make_tfmn_args_ls(), 
     key_vars_chr = character(0), key_totals_ls = NULL, metrics_chr = make_metric_vars(), 
     models_chr = c("Mean", "Naïve", "Seasonal naïve", "Drift", 
-        "Trend", "LMTS", "ETS", "ARIMA", "NNTEAR", "Prophet", 
+        "Trend", "LMTS", "ETS", "ARIMA", "NENTAR", "Prophet", 
         "Reg_ARIMA", "Reg_Prophet", "Reg_TSLM"), model_type_1L_chr = "multiplicative", 
     order_1L_int = 2, period_1L_int = 12, predictors_chr = character(0), 
     prefix_1L_chr = "Cumulative", stepwise_1L_lgl = TRUE, terms_1L_chr = character(0), 
@@ -1634,8 +1634,8 @@ make_ts_models <- function (data_xx, approximation_xx = NULL, collapse_1L_lgl =
                 LMTS = fable::TSLM(!!rlang::sym(.x) ~ trend() + 
                   season()), ETS = fable::ETS(!!rlang::sym(.x)), 
                 ARIMA = fable::ARIMA(!!rlang::sym(.x), approximation = approximation_xx, 
-                  stepwise = stepwise_1L_lgl), NNTEAR = fable::NNETAR(!!rlang::sym(.x)), 
-                Prophet = eval(parse(text = paste0("fable.prophet::prophet(", 
+                  stepwise = stepwise_1L_lgl), NNETAR = fable::NNETAR(!!rlang::sym(.x)), 
+                NNTEAR = fable::NNETAR(!!rlang::sym(.x)), Prophet = eval(parse(text = paste0("fable.prophet::prophet(", 
                   .x, "~season(period=", period_1L_int, ", type='", 
                   model_type_1L_chr, "', order=", order_1L_int, 
                   "))"))))

data-raw/fns/make.R

@@ -1057,7 +1057,9 @@ make_ts_models <- function (data_xx, approximation_xx = NULL, collapse_1L_lgl =
                                                  "yearly"), index_1L_chr = character(0), join_to_args_ls = make_tfmn_args_ls(), key_vars_chr = character(0),
                             key_totals_ls = NULL, metrics_chr = make_metric_vars(), models_chr = c("Mean",
                                                                                                    "Naïve", "Seasonal naïve", "Drift", "Trend", "LMTS",
-                                                                                                   "ETS", "ARIMA", "NNTEAR", "Prophet", "Reg_ARIMA", "Reg_Prophet","Reg_TSLM"),
+                                                                                                   "ETS", "ARIMA", "NENTAR",
+                                                                                                   # "NNTEAR",
+                                                                                                   "Prophet", "Reg_ARIMA", "Reg_Prophet","Reg_TSLM"),
                             model_type_1L_chr = "multiplicative", order_1L_int = 2, period_1L_int = 12, predictors_chr = character(0), prefix_1L_chr = "Cumulative",
                             stepwise_1L_lgl = TRUE, terms_1L_chr = character(0), terms_ls = NULL, test_1L_int = integer(0),
                             type_1L_chr = c("totals", "key"), what_1L_chr = character(0)) {
@@ -1119,7 +1121,8 @@ make_ts_models <- function (data_xx, approximation_xx = NULL, collapse_1L_lgl =
                             LMTS = fable::TSLM(!!rlang::sym(.x) ~ trend() + season()),
                             ETS = fable::ETS(!!rlang::sym(.x)), ARIMA = fable::ARIMA(!!rlang::sym(.x),
                                                                                      approximation = approximation_xx, stepwise = stepwise_1L_lgl),
-                            NNTEAR = fable::NNETAR(!!rlang::sym(.x)),
+                            NNETAR = fable::NNETAR(!!rlang::sym(.x)),
+                            NNTEAR = fable::NNETAR(!!rlang::sym(.x)), # Purely for legacy reasons
                             Prophet = eval(parse(text = paste0("fable.prophet::prophet(",
                                                                .x, "~season(period=", period_1L_int, ", type='",
                                                                model_type_1L_chr, "', order=", order_1L_int,