From 3940eb4c0a241672cfcc35233385bd5bd25137c2 Mon Sep 17 00:00:00 2001
From: Matt Dancho <mdancho@gmail.com>
Date: Fri, 12 Jul 2024 10:51:08 -0400
Subject: [PATCH] fix cran glmnet not imported issue

---
 CRAN-SUBMISSION            | 6 +++---
 DESCRIPTION                | 8 +++-----
 NAMESPACE                  | 1 +
 NEWS.md                    | 2 ++
 R/00_imports.R             | 1 +
 R/ensemble_model_spec.R    | 1 +
 man/ensemble_model_spec.Rd | 1 +
 7 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
index 7804d18..ad81161 100644
--- a/CRAN-SUBMISSION
+++ b/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
diff --git a/DESCRIPTION b/DESCRIPTION
index 12d2dd5..c8f5a92 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 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 = "mdancho@business-science.io", role = c("aut", "cre")),
     person("Business Science", role = "cph")
@@ -9,8 +9,7 @@ Authors@R: c(
 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,
diff --git a/NAMESPACE b/NAMESPACE
index 95eb3f4..8a4664a 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -38,6 +38,7 @@ export(ensemble_weighted)
 export(expr)
 export(sym)
 export(syms)
+import(glmnet)
 import(modeltime)
 import(modeltime.resample)
 importFrom(magrittr,"%>%")
diff --git a/NEWS.md b/NEWS.md
index 72a0f82..3791811 100644
--- a/NEWS.md
+++ b/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 
diff --git a/R/00_imports.R b/R/00_imports.R
index 7c45bd5..29943ed 100644
--- a/R/00_imports.R
+++ b/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
diff --git a/R/ensemble_model_spec.R b/R/ensemble_model_spec.R
index 6ab221b..41989b8 100644
--- a/R/ensemble_model_spec.R
+++ b/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) %>%
diff --git a/man/ensemble_model_spec.Rd b/man/ensemble_model_spec.Rd
index 5b1556c..9107fb8 100644
--- a/man/ensemble_model_spec.Rd
+++ b/man/ensemble_model_spec.Rd
@@ -105,6 +105,7 @@ library(modeltime)
 library(modeltime.ensemble)
 library(dplyr)
 library(timetk)
+library(glmnet)
 
 # Step 1: Make resample predictions for submodels
 resamples_tscv <- training(m750_splits) \%>\%