update pkgdown

.github/workflows/R-CMD-check.yaml

@@ -28,13 +28,11 @@ jobs:
       fail-fast: false
       matrix:
         config:
-          - {os: macOS-latest,   r: 'release'}
+          - {os: macos-latest,   r: 'release'}
           - {os: windows-latest, r: 'release'}
-          - {os: windows-latest, r: '3.6'}
-          - {os: ubuntu-18.04,   r: 'devel', rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest", http-user-agent: "R/4.0.0 (ubuntu-18.04) R (4.0.0 x86_64-pc-linux-gnu x86_64 linux-gnu) on GitHub Actions" }
-          - {os: ubuntu-18.04,   r: 'release', rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest"}
-          - {os: ubuntu-18.04,   r: 'oldrel',  rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest"}
-          - {os: ubuntu-18.04,   r: '3.5',     rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest"}
+          - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
+          - {os: ubuntu-latest,   r: 'release'}
+          - {os: ubuntu-latest,   r: 'oldrel-1'}
 
     env:
       RSPM: ${{ matrix.config.rspm }}
@@ -64,11 +62,6 @@ jobs:
           extra-packages: any::rcmdcheck
           needs: check
 
-      - uses: r-lib/actions/setup-r-dependencies@v2
-        with:
-          extra-packages: any::rcmdcheck
-          needs: check
-
       - uses: r-lib/actions/check-r-package@v2
         with:
           upload-snapshots: true

.github/workflows/pkgdown.yaml

@@ -24,6 +24,13 @@ jobs:
     steps:
       - uses: actions/checkout@v3
 
+      - name: Install miniconda r-gluonts env
+        uses: conda-incubator/setup-miniconda@v3
+        with:
+          activate-environment: r-gluonts
+          python-version: 3.7.1
+          environment-file: _environment.yml
+
       - uses: r-lib/actions/setup-pandoc@v2
 
       - uses: r-lib/actions/setup-r@v2

README.Rmd

@@ -16,7 +16,7 @@ knitr::opts_chunk$set(
 )
 ```
 
-# modeltime.gluonts <img src='man/figures/logo-modeltime-gluonts.png' align="right" height="138" />
+# modeltime.gluonts <a href="https://business-science.github.io/modeltime.gluonts/"><img src="man/figures/logo.png" align="right" height="138" alt="modeltime.gluonts website" /></a>
 
 <!-- badges: start -->
 [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/modeltime.gluonts)](https://cran.r-project.org/package=modeltime)

README.md

@@ -1,11 +1,11 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-# modeltime.gluonts <img src='man/figures/logo-modeltime-gluonts.png' align="right" height="138" />
+# modeltime.gluonts <a href="https://business-science.github.io/modeltime.gluonts/"><img src="man/figures/logo.png" align="right" height="138" alt="modeltime.gluonts website" /></a>
 
 <!-- badges: start -->
 
-[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/modeltime.gluonts)](https://cran.r-project.org/package=modeltime)
+[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/modeltime.gluonts)](https://cran.r-project.org/package=modeltime)
 ![](http://cranlogs.r-pkg.org/badges/modeltime.gluonts?color=brightgreen)
 ![](http://cranlogs.r-pkg.org/badges/grand-total/modeltime.gluonts?color=brightgreen)
 [![Codecov test
@@ -94,23 +94,23 @@ The modeltime ecosystem is growing
 Modeltime is part of a **growing ecosystem** of Modeltime forecasting
 packages.
 
--   [Modeltime (Machine
-    Learning)](https://business-science.github.io/modeltime/)
+- [Modeltime (Machine
+  Learning)](https://business-science.github.io/modeltime/)
 
--   [Modeltime H2O
-    (AutoML)](https://business-science.github.io/modeltime.h2o/)
+- [Modeltime H2O
+  (AutoML)](https://business-science.github.io/modeltime.h2o/)
 
--   [Modeltime GluonTS (Deep
-    Learning)](https://business-science.github.io/modeltime.gluonts/)
+- [Modeltime GluonTS (Deep
+  Learning)](https://business-science.github.io/modeltime.gluonts/)
 
--   [Modeltime Ensemble (Blending
-    Forecasts)](https://business-science.github.io/modeltime.ensemble/)
+- [Modeltime Ensemble (Blending
+  Forecasts)](https://business-science.github.io/modeltime.ensemble/)
 
--   [Modeltime Resample
-    (Backtesting)](https://business-science.github.io/modeltime.resample/)
+- [Modeltime Resample
+  (Backtesting)](https://business-science.github.io/modeltime.resample/)
 
--   [Timetk (Feature Engineering, Data Wrangling, Time Series
-    Visualization)](https://business-science.github.io/timetk/)
+- [Timetk (Feature Engineering, Data Wrangling, Time Series
+  Visualization)](https://business-science.github.io/timetk/)
 
 ## Take the High-Performance Forecasting Course
 
@@ -140,17 +140,17 @@ Series Forecasting
 Course**](https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting).
 You will learn:
 
--   **Time Series Machine Learning** (cutting-edge) with `Modeltime` -
-    30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)
--   **Deep Learning** with `GluonTS` (Competition Winners)
--   **Time Series Preprocessing**, Noise Reduction, & Anomaly Detection
--   **Feature engineering** using lagged variables & external regressors
--   **Hyperparameter Tuning**
--   **Time series cross-validation**
--   **Ensembling** Multiple Machine Learning & Univariate Modeling
-    Techniques (Competition Winner)
--   **Scalable Forecasting** - Forecast 1000+ time series in parallel
--   and more.
+- **Time Series Machine Learning** (cutting-edge) with `Modeltime` - 30+
+  Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)
+- **Deep Learning** with `GluonTS` (Competition Winners)
+- **Time Series Preprocessing**, Noise Reduction, & Anomaly Detection
+- **Feature engineering** using lagged variables & external regressors
+- **Hyperparameter Tuning**
+- **Time series cross-validation**
+- **Ensembling** Multiple Machine Learning & Univariate Modeling
+  Techniques (Competition Winner)
+- **Scalable Forecasting** - Forecast 1000+ time series in parallel
+- and more.
 
 <p class="text-center" style="font-size:24px;">
 Become the Time Series Expert for your organization.

_pkgdown.yml

@@ -1,17 +1,12 @@
+url: https://business-science.github.io/modeltime.gluonts/
 template:
   bootstrap: 5
-  bslib:
-    base_font:
-      google: Roboto
-    heading_font:
-      google: Montserrat
-    # code_font:
-    #   google: "JetBrains Mono"
+  bootswatch: lux
   params:
-    bootswatch: flatly
-    ganalytics: UA-76139189-2
+    ganalytics: G-20GDZ5LL77
 
 navbar:
+  bg: primary
   title: "modeltime.gluonts"
   left:
     - icon: fa-home
@@ -60,6 +55,11 @@ navbar:
           href: https://business-science.github.io/modeltime.h2o/
         - text: 'Modeltime GluonTS (Deep Learning)'
           href: https://business-science.github.io/modeltime.gluonts/
+    - text: Python
+      menu:
+      - text: Forecast
+      - text: Timetk for Python (Time Series Analysis)
+        href: https://business-science.github.io/pytimetk/
     - icon: fas fa-graduation-cap
       text: "Learn"
       href: https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting/