fix cran glmnet not imported issue

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
-Version: 1.0.2
-Date: 2022-10-18 17:35:41 UTC
-SHA: 9a1335f085fa305e0958c3fef093ad66cb00ac0e
+Version: 1.0.4
+Date: 2024-06-07 19:39:45 UTC
+SHA: 6c33f8845695356a2d481b2a92b5dc89649bf9e3

DESCRIPTION

@@ -1,16 +1,15 @@
 Package: modeltime.ensemble
 Type: Package
 Title: Ensemble Algorithms for Time Series Forecasting with Modeltime
-Version: 1.0.3.9000
+Version: 1.0.4
 Authors@R: c(
     person("Matt", "Dancho", email = "[email protected]", role = c("aut", "cre")),
     person("Business Science", role = "cph")
     )
 Description: 
     A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, 
     weighted averaging, and stacking. These techniques are popular methods 
-    to improve forecast accuracy and stability. Refer to papers such as 
-    "Machine-Learning Models for Sales Time Series Forecasting" Pavlyshenko, B.M. (2019) <doi:10.3390>.
+    to improve forecast accuracy and stability. 
 URL: https://business-science.github.io/modeltime.ensemble/, https://github.com/business-science/modeltime.ensemble
 BugReports: https://github.com/business-science/modeltime.ensemble/issues
 License: MIT + file LICENSE
@@ -24,7 +23,6 @@ Imports:
     rsample,
     yardstick,
     workflows (>= 0.2.1),
-    parsnip (>= 0.1.6),
     recipes (>= 0.1.15),
     timetk (>= 2.5.0),
     tibble,
@@ -40,10 +38,10 @@ Imports:
     parallel, 
     doParallel,
     foreach,
+    glmnet
 Suggests:
     gt,
     dials,
-    glmnet,
     utils,
     earth,
     testthat,

NAMESPACE

@@ -38,6 +38,7 @@ export(ensemble_weighted)
 export(expr)
 export(sym)
 export(syms)
+import(glmnet)
 import(modeltime)
 import(modeltime.resample)
 importFrom(magrittr,"%>%")

NEWS.md

@@ -1,5 +1,7 @@
 # modeltime.ensemble (development version)
 
+# modeltime.ensemble 1.0.4
+
 - #31 Fixes issue with metric argument not being specified:
 
 ```r 

R/00_imports.R

@@ -1,5 +1,6 @@
 #' @import modeltime
 #' @import modeltime.resample
+#' @import glmnet
 #' @importFrom modeltime mdl_time_forecast modeltime_calibrate modeltime_refit mdl_time_refit get_model_description default_forecast_accuracy_metric_set summarize_accuracy_metrics
 #' @importFrom yardstick rmse rsq
 #' @importFrom tune control_grid

R/ensemble_model_spec.R

@@ -85,6 +85,7 @@
 #' library(modeltime.ensemble)
 #' library(dplyr)
 #' library(timetk)
+#' library(glmnet)
 #'
 #' # Step 1: Make resample predictions for submodels
 #' resamples_tscv <- training(m750_splits) %>%