rebuild landing page

Author: nmdefries

README.Rmd

@@ -145,13 +145,13 @@ Below the fold, we pull the dataset from the epidata API and clean it.
 <summary> Creating the dataset using `{epidatr}` and `{epiprocess}` </summary>
 
 This section is intended to demonstrate some of the ubiquitous cleaning operations needed to be able to forecast.
-A subset of this dataset prepared here is also included ready-to-go in [`{epipredict}`](https://cmu-delphi.github.io/epipredict/) as `covid_case_death_rates`.
+A subset of the dataset prepared here is also included ready-to-go in [`{epipredict}`](https://cmu-delphi.github.io/epipredict/) as `covid_case_death_rates`.
 
 First we pull both `jhu-csse` cases and deaths data from the
 [Delphi API](https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html) using the
 [`{epidatr}`](https://cmu-delphi.github.io/epidatr/) package:
 
-```{r case_death, warning = FALSE, eval = FALSE}
+```{r case_death, warning = FALSE, eval = TRUE}
 cases <- pub_covidcast(
   source = "jhu-csse",
   signals = "confirmed_7dav_incidence_prop",
@@ -287,7 +287,7 @@ four_week_ahead <- arx_forecaster(
 four_week_ahead
 ```
 
-In our model setup, we are defining as our predictors case rate lagged 0-3
+In our model setup, we are defining as predictors case rate lagged 0-3
 days, one week, and two weeks, and death rate lagged 0-2 weeks.
 The result `four_week_ahead` is both a fitted model object which could be used
 any time in the future to create different forecasts, and a set of predicted
@@ -339,8 +339,8 @@ For this particular day and these locations, the forecasts are relatively
 accurate, with the true data being at least within the 10-90% interval.
 A couple of things to note:
 
-1. Our methods are primarily direct forecasters; this means we don't need to
-   predict 1, 2,..., 27 days ahead to then predict 28 days ahead
+1. `epipredict` methods are primarily direct forecasters; this means we don't need to
+   predict 1, 2,..., 27 days ahead to then predict 28 days ahead.
 2. All of our existing engines are geo-pooled, meaning the training data is
    shared across geographies. This has the advantage of increasing the amount of
    available training data, with the restriction that the data needs to be on

README.md

@@ -86,7 +86,7 @@ Creating the dataset using `{epidatr}` and `{epiprocess}`
 </summary>
 
 This section is intended to demonstrate some of the ubiquitous cleaning
-operations needed to be able to forecast. A subset of this dataset
+operations needed to be able to forecast. A subset of the dataset
 prepared here is also included ready-to-go in
 [`{epipredict}`](https://cmu-delphi.github.io/epipredict/) as
 `covid_case_death_rates`.
@@ -234,7 +234,7 @@ four_week_ahead <- arx_forecaster(
 four_week_ahead
 #> ══ A basic forecaster of type ARX Forecaster ════════════════════════════════
 #> 
-#> This forecaster was fit on 2025-03-03 14:43:07.
+#> This forecaster was fit on 2025-03-04 12:12:54.
 #> 
 #> Training data was an <epi_df> with:
 #> • Geography: state,
@@ -251,8 +251,8 @@ four_week_ahead
 #> 
 ```
 
-In our model setup, we are defining as our predictors case rate lagged
-0-3 days, one week, and two weeks, and death rate lagged 0-2 weeks. The
+In our model setup, we are defining as predictors case rate lagged 0-3
+days, one week, and two weeks, and death rate lagged 0-2 weeks. The
 result `four_week_ahead` is both a fitted model object which could be
 used any time in the future to create different forecasts, and a set of
 predicted values (and prediction intervals) for each location 28 days
@@ -298,12 +298,12 @@ four_week_ahead$predictions |>
 #> # A tibble: 20 × 5
 #>   geo_value values quantile_levels forecast_date target_date
 #>   <chr>      <dbl>           <dbl> <date>        <date>     
-#> 1 ca        0.0425            0.1  2021-08-01    2021-08-29 
-#> 2 ca        0.0803            0.25 2021-08-01    2021-08-29 
-#> 3 ca        0.115             0.5  2021-08-01    2021-08-29 
-#> 4 ca        0.150             0.75 2021-08-01    2021-08-29 
-#> 5 ca        0.187             0.9  2021-08-01    2021-08-29 
-#> 6 ma        0                 0.1  2021-08-01    2021-08-29 
+#> 1 ca        0.199             0.1  2021-08-01    2021-08-29 
+#> 2 ca        0.285             0.25 2021-08-01    2021-08-29 
+#> 3 ca        0.345             0.5  2021-08-01    2021-08-29 
+#> 4 ca        0.405             0.75 2021-08-01    2021-08-29 
+#> 5 ca        0.491             0.9  2021-08-01    2021-08-29 
+#> 6 ma        0.0285            0.1  2021-08-01    2021-08-29 
 #> # ℹ 14 more rows
 ```
 
@@ -313,8 +313,9 @@ For this particular day and these locations, the forecasts are
 relatively accurate, with the true data being at least within the 10-90%
 interval. A couple of things to note:
 
-1.  Our methods are primarily direct forecasters; this means we don’t
-    need to predict 1, 2,…, 27 days ahead to then predict 28 days ahead
+1.  `epipredict` methods are primarily direct forecasters; this means we
+    don’t need to predict 1, 2,…, 27 days ahead to then predict 28 days
+    ahead.
 2.  All of our existing engines are geo-pooled, meaning the training
     data is shared across geographies. This has the advantage of
     increasing the amount of available training data, with the