update docs version 2.6.2

README.Rmd

@@ -26,7 +26,7 @@ devtools::load_all()
 [![R-CMD-check](https://github.com/business-science/timetk/workflows/R-CMD-check/badge.svg)](https://github.com/business-science/timetk/actions)
 [![codecov](https://codecov.io/gh/business-science/timetk/branch/master/graph/badge.svg)](https://app.codecov.io/gh/business-science/timetk)
 
->  Visualize, wrangle, and transform time series data using the `tidyverse`
+>  Time series analysis using the `tidyverse`
 
 
 ## Installation
@@ -61,14 +61,15 @@ There are _many_ R packages for working with Time Series data. Here's how `timet
 | Static Plots (ggplot) | ✅ | :x: | ✅ | :x: | 
 | [Time Series](https://business-science.github.io/timetk/articles/TK04_Plotting_Time_Series.html) | ✅ | :x: | ✅ | :x: | 
 | [Correlation, Seasonality](https://business-science.github.io/timetk/articles/TK05_Plotting_Seasonality_and_Correlation.html) | ✅ | :x: | ✅ | :x: | 
-| [Anomaly Detection](https://business-science.github.io/timetk/articles/TK08_Automatic_Anomaly_Detection.html) | ✅ | :x: | :x: | :x: | 
 | [__Data Wrangling__](https://business-science.github.io/timetk/articles/TK07_Time_Series_Data_Wrangling.html) | | | | | 
 | Time-Based Summarization | ✅ | :x: | :x: | ✅ | 
 | Time-Based Filtering | ✅ | :x: | :x: | ✅ | 
 | Padding Gaps | ✅ | ✅ | :x: | :x: | 
 | Low to High Frequency | ✅ | :x: | :x: | :x: | 
 | Imputation | ✅ | ✅ | :x: | :x: | 
 | Sliding / Rolling | ✅ | ✅ | :x: | ✅ | 
+| __Machine Learning__ | | | | |
+| [Anomaly Detection](https://business-science.github.io/timetk/articles/TK08_Automatic_Anomaly_Detection.html) | ✅ | :x: | :x: | :x: | 
 | [__Feature Engineering (recipes)__](https://business-science.github.io/timetk/articles/TK03_Forecasting_Using_Time_Series_Signature.html) | | | | | 
 | Date Feature Engineering | ✅ | :x: | :x: | :x: | 
 | Holiday Feature Engineering | ✅ | :x: | :x: | :x: | 

README.md

@@ -11,8 +11,7 @@
 [![R-CMD-check](https://github.com/business-science/timetk/workflows/R-CMD-check/badge.svg)](https://github.com/business-science/timetk/actions)
 [![codecov](https://codecov.io/gh/business-science/timetk/branch/master/graph/badge.svg)](https://app.codecov.io/gh/business-science/timetk)
 
-> Visualize, wrangle, and transform time series data using the
-> `tidyverse`
+> Time series analysis using the `tidyverse`
 
 ## Installation
 
@@ -46,14 +45,15 @@ data frames or tibbles).
 | Static Plots (ggplot) | ✅ | :x: | ✅ | :x: |
 | [Time Series](https://business-science.github.io/timetk/articles/TK04_Plotting_Time_Series.html) | ✅ | :x: | ✅ | :x: |
 | [Correlation, Seasonality](https://business-science.github.io/timetk/articles/TK05_Plotting_Seasonality_and_Correlation.html) | ✅ | :x: | ✅ | :x: |
-| [Anomaly Detection](https://business-science.github.io/timetk/articles/TK08_Automatic_Anomaly_Detection.html) | ✅ | :x: | :x: | :x: |
 | [**Data Wrangling**](https://business-science.github.io/timetk/articles/TK07_Time_Series_Data_Wrangling.html) | | | | |
 | Time-Based Summarization | ✅ | :x: | :x: | ✅ |
 | Time-Based Filtering | ✅ | :x: | :x: | ✅ |
 | Padding Gaps | ✅ | ✅ | :x: | :x: |
 | Low to High Frequency | ✅ | :x: | :x: | :x: |
 | Imputation | ✅ | ✅ | :x: | :x: |
 | Sliding / Rolling | ✅ | ✅ | :x: | ✅ |
+| **Machine Learning** | | | | |
+| [Anomaly Detection](https://business-science.github.io/timetk/articles/TK08_Automatic_Anomaly_Detection.html) | ✅ | :x: | :x: | :x: |
 | [**Feature Engineering (recipes)**](https://business-science.github.io/timetk/articles/TK03_Forecasting_Using_Time_Series_Signature.html) | | | | |
 | Date Feature Engineering | ✅ | :x: | :x: | :x: |
 | Holiday Feature Engineering | ✅ | :x: | :x: | :x: |

_pkgdown.yml

@@ -18,8 +18,6 @@ navbar:
  href: articles/TK04_Plotting_Time_Series.html
  - text: "Plotting Seasonality and Correlation"
  href: articles/TK05_Plotting_Seasonality_and_Correlation.html
- - text: "Automatic Anomaly Detection"
- href: articles/TK08_Automatic_Anomaly_Detection.html
  - text: '---'
  - text: 'Data Wrangling'
  - text: "Time Series Data Wrangling"
@@ -28,6 +26,8 @@ navbar:
  - text: "Machine Learning"
  - text: "Time Series Machine Learning"
  href: articles/TK03_Forecasting_Using_Time_Series_Signature.html
+ - text: "Anomaly Detection"
+ href: articles/TK08_Automatic_Anomaly_Detection.html
  - text: '---'
  - text: 'Time Series Fundamentals'
  - text: "What is the Time Series Signature?"
@@ -38,7 +38,7 @@ navbar:
  - text: "Sequences & Data Structures"
  - text: "Intelligent Date & Time Sequences"
  href: articles/TK02_Time_Series_Date_Sequences.html
- - text: "Time Series Conversion (ts, zoo, & xts)"
+ - text: "Time Series Class Conversion (tbl, ts, zoo, & xts)"
  href: articles/TK00_Time_Series_Coercion.html
  - text: "API"
  href: reference/index.html