update NEWS for v.0.1.3

NEWS.md

@@ -1,16 +1,38 @@
-## Changes since last release
+## covidHubUtils 0.1.3
 
-- Minor updates to overview vignette.
+This is a release focusing on new scoring function and truth-processing functions. The release also contains new feature updates and bug fixes in other util functions. 
+
+covidHubUtils now requires the `scoringutils` package version to be at least 0.1.5.
+
+### Breaking changes
 - `score_forecasts()` is now implemented for quantile-format forecasts to compute absolute error, weighted interval score, sharpness, overprediction, underprediction, and prediction interval coverage at any specified quantile.  Minimally one should have the `forecasts` dataframe produced by `load_forecasts()` and the truth dataframe produced by `load_truth()` to calculate scores. If one desires to specify a subset of all available scores, one should consult [this reference](https://epiforecasts.io/scoringutils/reference/eval_forecasts.html#details) for valid scores in the `desired_score_types` vector.
-- wis calculation changed to reflect preferred weighting scheme for interval scores.
-- Refactor `load_latest_forecasts_repo`, splitting out functionality for reading in forecasts into a new exported function `load_forecast_files_repo` that loads specific forecast files.
-- More user friendly color palettes when plotting a small number of intervals.
-- Add `preprocess_truth_for_zoltar()` and `save_truth_for_zoltar()` for generating truth file for zoltar.
+
+  + wis calculation changed to reflect preferred weighting scheme for interval scores.
+
+
+- `preprocess_truth_for_zoltar()` and `save_truth_for_zoltar()` are now implemented to create standard cumulative and incident death truth csv files for Zoltar.
+
+- `preprocess_hospitalization()` is now implemented to create standard cumulative and incident hospitalization truth csv files.
+
+### Feature updates
+
+- Update `load_forecasts()` and `load_latest_forecasts()`
+
+  + Update default value of `forecast_date_window_size` to 0 in`load_latest_forecasts()` so that it looks for forecasts on the `latest_forecast_date` only.
+
+  + Refactor `load_latest_forecasts_repo()`, splitting out functionality for reading in forecasts into a new exported function `load_forecast_files_repo()` that loads specific forecast files.
+
+  + Standardize data format and columns types of the output.
+
+  + Fix validation bug for `forecast_dates` when loading forecasts from zoltar. Loading functions will throw an error if all dates in `forecast_dates` are invalid forecast dates in Zoltar.
+
+- Update `plot_forecast()` to use more user-friendly color palettes when plotting a small number of intervals.
+
 - Update `get_model_designations()` to return `NA` when model designations for outdated models are not available on Zoltar.
-- Update default value of `forecast_date_window_size` to 0 in`load_latest_forecasts()` so that it looks for forecasts on the `latest_forecast_date` only.
-- `preprocess_hospitalization()` is now implemented. This method creates standard cumulative and incident truth csv files for hospitalization.
-- Standardize data format and columns types of the output from loading functions. 
-- Fix validation bug for `forecast_dates` when loading forecasts from zoltar. Loading functions will throw an error if all dates in `forecast_dates` are invalid forecast dates in Zoltar.
+
+### Package updates
+- There is no backwards compatibility.
+- Minor updates to overview vignette.
 
 ## covidHubUtils 0.1.2
 
@@ -20,19 +42,19 @@ This is a release focusing on new features in scoring functions and plotting fun
 
 - Update `plot_forecast()`
 
-  Set `truth_source` to be optional when the user provides `truth_data`. However, it is still needed when `show_caption = TRUE`.
+  + Set `truth_source` to be optional when the user provides `truth_data`. However, it is still needed when `show_caption = TRUE`.
 
-  Remove format validation for `model` column in user-provided `truth_data`.
+  + Remove format validation for `model` column in user-provided `truth_data`.
 
-  Support daily hospitalization plot. When `target_variable = "inc hosp"`, the user needs to provided `truth_data`. Otherwise, an error will be thrown. 
+  + Support daily hospitalization plot. When `target_variable = "inc hosp"`, the user needs to provided `truth_data`. Otherwise, an error will be thrown. 
 
-  Add `facet_nrow`, `facet_ncol`, `fill_transparency`, `title` and `subtitle`.
+  + Add `facet_nrow`, `facet_ncol`, `fill_transparency`, `title` and `subtitle`.
 
 - Update `get_plot_forecast_data()`
 
-  Remove format validation for `model` column in user-provided `truth_data`.
+  + Remove format validation for `model` column in user-provided `truth_data`.
 
-  When `target_variable = "inc hosp"`, the user needs to provided `truth_data`. Otherwise, an error will be thrown. 
+  + When `target_variable = "inc hosp"`, the user needs to provided `truth_data`. Otherwise, an error will be thrown. 
 
 ### Package updates
 - There is no backwards compatibility.
@@ -47,11 +69,11 @@ This is a release focusing on new features in plotting functions.
 ### Feature updates
 - `plot_forecast()` now supports faceted plots of multiple models, locations and forecast dates for one target variable. 
 
-  In `plot_forecast()`, `facet` and `facet_scales` are equivalent to `facets` and `scales` in `ggplot2::facet_wrap()`. `facet` takes facet formula, for example `facet = ~ model`. `facet_scales` are expecting the same values for `scales` in `ggplot2::facet_wrap()`, such as `"fixed"`, `"free_y"`, `"free_x"` or `"free"`.
+  + In `plot_forecast()`, `facet` and `facet_scales` are equivalent to `facets` and `scales` in `ggplot2::facet_wrap()`. `facet` takes facet formula, for example `facet = ~ model`. `facet_scales` are expecting the same values for `scales` in `ggplot2::facet_wrap()`, such as `"fixed"`, `"free_y"`, `"free_x"` or `"free"`.
 
-  If `fill_by_model = TRUE`, each model will be represented by a unique color. If `fill_by model = FALSE`, all models and selected prediction intervals will be represented by blue colors.
+  + If `fill_by_model = TRUE`, each model will be represented by a unique color. If `fill_by model = FALSE`, all models and selected prediction intervals will be represented by blue colors.
 
-  For simplicity, prediction interval legends will be grey in faceted plots. Morever, when the user selects more than 5 models, only 95% predicition interval is included. Otherwise, all selected prediction intervals will be plotted. 
+  + For simplicity, prediction interval legends will be grey in faceted plots. Morever, when the user selects more than 5 models, only 95% predicition interval is included. Otherwise, all selected prediction intervals will be plotted. 
 
 ### Package updates
 - There is no backwards compatibility due to argument changes in `plot_forecast()`.