Mermaid diagram and other documentation updates

DESCRIPTION

@@ -26,7 +26,8 @@ Imports:
     projections,
     incidence,
     jsonlite
-Suggests: 
+Suggests:
+    DiagrammeR, 
     knitr,
     rmarkdown,
     devtools,

vignettes/acciddasuite.Rmd

@@ -20,17 +20,27 @@ knitr::opts_chunk$set(
 
 # Introduction
 
-The `acciddasuite` package provides tools for building infectious disease forecasts and relies on the [`fable`](https://fable.tidyverts.org/) framework.
+The `acciddasuite` package provides tools for building infectious disease forecasts and relies on the [`fable`](https://fable.tidyverts.org/) modeling framework. The overall goal is to provide public health professionals with an easily-adoptable approach to generating an ensemble of outputs from statistical models, evaluating forecasts, and visualizing outputs.   
 
-This vignette demonstrates a basic example of generating and evaluating forecasts following the standard forecasting workflow described by [Hyndman & Athanasopoulos (2021)](https://otexts.com/fpp3/basic-steps.html).
+This vignette demonstrates a basic example of generating, evaluating, and visualizing forecasts following the standard forecasting workflow described by [Hyndman & Athanasopoulos (2021)](https://otexts.com/fpp3/basic-steps.html). 
+
+Updated forecasting package information can be found [here](https://robjhyndman.com/hyndsight/forecast9.html).
 
 # Forecasting Workflow
 
-## `get_data`
+## `Forecast Planning`
+
+**To get more information about how to know whether forecasting is the best approach for your task, follow the steps in [this](forecast_planning.html) article.**
+
+## `Time Series Data`
+
+The first step of generating disease forecasts is providing time series/surveillance data; the data that the mdoel will assume has already happened.
 
 **If you would like to load your own surveillance, you can follow [these](external_data.html) steps for formatting.**
 
-For demonstration purposes, we will load surveillance data from the [CDC National Health Safety Network](https://data.cdc.gov/Public-Health-Surveillance/Weekly-Hospital-Respiratory-Data-HRD-Metrics-by-Ju/mpgq-jmmr/about_data). The `get_data()` function provides a convenient interface to access this data using the [`epidatr`](https://cmu-delphi.github.io/epidatr/) package.
+For demonstration purposes, we will load surveillance data from the [CDC National Health Safety Network](https://data.cdc.gov/Public-Health-Surveillance/Weekly-Hospital-Respiratory-Data-HRD-Metrics-by-Ju/mpgq-jmmr/about_data)using `acciddasuite`'s `get_data()` function. The data dictionary is available [here](https://dev.socrata.com/foundry/data.cdc.gov/mpgq-jmmr).
+
+The `get_data()` function provides a convenient interface to access this data using the [`epidatr`](https://cmu-delphi.github.io/epidatr/) package.
 
 ```{r, get_data}
 library(dplyr)
@@ -40,13 +50,17 @@ library(acciddasuite)
 df <- get_data(pathogen = "covid", geo_values = "nc")
 head(df)
 
+df <- get_data(pathogen = "flu", geo_values = "ny")
+
+head(df)
 ```
 
 ```{r, to_csv, echo = FALSE}
 df |> write.csv("example_data.csv", row.names = FALSE)
 ```
 
-To look at what `df` looks like, you can access the example `csv` file here: [example_data.csv](https://github.com/ACCIDDA/acciddasuite/blob/main/example_data.csv).
+To examine `df` in more detail, you can access the example `csv` file here: [example_data.csv](https://github.com/ACCIDDA/acciddasuite/blob/main/example_data.csv).
+
 
 ## Time Series Cross-Validation
 
@@ -60,10 +74,14 @@ We visualize the data and decide on the `eval_start_date`.
 eval_start_date <- max(df$target_end_date) - 90
 ```
 
-Default models are:  
+Default models are:
+
 * `SNAIVE` (Seasonal Naïve): Assumes this week will look like the same week last year. The simplest possible baseline.
+
 * `ETS` (Exponential Smoothing): A weighted average where recent weeks matter more than older ones. Adapts to trends and seasonal patterns.
+
 * `THETA`: Splits the data into a long-term trend and short-term fluctuations, forecasts each separately, then combines them.
+
 * `ARIMA`: Learns repeating patterns from past values to predict future ones. Auto-configured to find the best fit.
 
 ```{r, models}
@@ -83,9 +101,15 @@ Visualize forecasts by accessing the `plot` element of the forecast object:
 fcast$plot
 ```
 
+View forecast evaluation by viewing the `score` element of the object:
+```{r, score-forecast}
+fcast$score
+```
+
+
 ## Adding `extra_models`
 
-Additonal models can be added by defining them in a list and passing them to `get_fcast()`. The models should be compatible with the fable framework (see [fable documentation](https://fabletools.tidyverts.org/articles/extension_models.html) for more information).
+Additional models can be added by defining them in a list and passing them to `get_fcast()`. The models should be compatible with the fable framework (see [fable documentation](https://fabletools.tidyverts.org/articles/extension_models.html) for more information).
 
 ```{r, extra-models}
 library(fable)
@@ -107,7 +131,6 @@ fcast = get_fcast(
 ```
 
 You can check how long each step took by calling `pipetime::get_log()`:
-
 ```{r, timing}
 get_log()
 ```