doc: update NEWS

Author: dshemetov

NEWS.md

@@ -4,34 +4,40 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 
 # epipredict 0.1
 
--   add `check_enough_train_data` that will error if training data is too small
--   added `check_enough_train_data` to `arx_forecaster`
--   simplify `layer_residual_quantiles()` to avoid timesuck in `utils::methods()`
--   rename the `dist_quantiles()` to be more descriptive, breaking change
--   removes previous `pivot_quantiles()` (now `*_wider()`, breaking change)
--   add `pivot_quantiles_wider()` for easier plotting
--   add complement `pivot_quantiles_longer()`
--   add `cdc_baseline_forecaster()` and `flusight_hub_formatter()`
--   add `smooth_quantile_reg()`
--   improved printing of various methods / internals
--   canned forecasters get a class
--   fixed quantile bug in `flatline_forecaster()`
--   add functionality to output the unfit workflow from the canned forecasters
--   add quantile_reg()
--   clean up documentation bugs
--   add smooth_quantile_reg()
--   add classifier
--   training window step debugged
--   `min_train_window` argument removed from canned forecasters
--   add forecasters
--   implement postprocessing
--   vignettes avaliable
--   arx_forecaster
--   pkgdown
--   Publish public for easy navigation
--   Two simple forecasters as test beds
--   Working vignette
--   use `checkmate` for input validation
--   refactor quantile extrapolation (possibly creates different results)
--   force `target_date` + `forecast_date` handling to match the time_type of 
-    the epi_df. allows for annual and weekly data
+- improve `ahead`, `target_date` and `forecast_date` default value handling in
+  `arx_forecaster()`, `arx_classifier()`, `cdc_baseline_forecaster()`, and
+  `flatline_forecaster()`; if `target_date` is provided and `ahead` is not,
+  `ahead` will be set to the difference between the `target_date` and
+  `forecast_date`
+- `layer_residual_quantiles()` will now error if any of the residual quantiles are NA
+- add `check_enough_train_data` that will error if training data is too small
+- added `check_enough_train_data` to `arx_forecaster`
+- simplify `layer_residual_quantiles()` to avoid timesuck in `utils::methods()`
+- rename the `dist_quantiles()` to be more descriptive, breaking change
+- removes previous `pivot_quantiles()` (now `*_wider()`, breaking change)
+- add `pivot_quantiles_wider()` for easier plotting
+- add complement `pivot_quantiles_longer()`
+- add `cdc_baseline_forecaster()` and `flusight_hub_formatter()`
+- add `smooth_quantile_reg()`
+- improved printing of various methods / internals
+- canned forecasters get a class
+- fixed quantile bug in `flatline_forecaster()`
+- add functionality to output the unfit workflow from the canned forecasters
+- add quantile_reg()
+- clean up documentation bugs
+- add smooth_quantile_reg()
+- add classifier
+- training window step debugged
+- `min_train_window` argument removed from canned forecasters
+- add forecasters
+- implement postprocessing
+- vignettes avaliable
+- arx_forecaster
+- pkgdown
+- Publish public for easy navigation
+- Two simple forecasters as test beds
+- Working vignette
+- use `checkmate` for input validation
+- refactor quantile extrapolation (possibly creates different results)
+- force `target_date` + `forecast_date` handling to match the time_type of
+  the epi_df. allows for annual and weekly data