NEWS.md

epipredict (development)

Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicate PR's.

epipredict 0.2.1

• Fix bug in flusight_hub_formatter() so that it works as expected even if the user has not first loaded the epidatasets package.

epipredict 0.2

Breaking changes

• Moved example datasets from being hosted in the package to being loaded from the epidatasets package. The datasets can no longer be loaded with data(<dataset name>), but can be accessed with data(<dataset name>, package = "epidatasets"), epidatasets::<dataset name> or, after loading the package, the name of the dataset alone (#382).
• step_adjust_latency() no longer allows empty column selection.
• Addresses upstream breaking changes from cmu-delphi/epiprocess#595 (growth_rate()). step_growth_rate() has lost its additional_gr_args_list argument and now has an na_rm argument.
• Moves epiprocess out of depends (#440). No internals have changed, but downstream users may need to add library(epiprocess) to existing code.
• Removes dependence on the distributional package, replacing the quantiles with hardhat::quantile_pred(). Some associated functions are deprecated with lifecycle messages.
• Rename check_enough_train_data() to check_enough_data(), and generalize it enough to use as a check on either training or testing.
• Add check for enough data to predict in arx_forecaster()
• Adds the .facet_filter option in epiprocess::autoplot() (cmu-delphi/epiprocess#647).

Improvements

• Add step_adjust_latency, which give several methods to adjust the forecast if the forecast_date is after the last day of data.
• Fix layer_population_scaling default by with other_keys.
• Make key column inference more consistent within the package and with current epiprocess.
• Fix quantile_reg() producing error when asked to output just median-level predictions.
• (temporary) ahead negative is allowed for step_epi_ahead until we have step_epi_shift
• Add reference_date as an argument to epi_recipe()
• Add step_climate() to create "climate" predictor in forecast workflows
• Add climatological_forecaster() to automatically create climate baselines
• Replace dist_quantiles() with hardhat::quantile_pred()
• Allow quantile() to threshold to an interval if desired (#434)
• arx_forecaster() detects if there's enough data to predict
• Add observed_response to autoplot so that forecasts can be plotted against the values they're predicting

Bug fixes

• Shifting no columns results in no error for either step_epi_ahead and step_epi_lag
• Quantiles produced by grf were sometimes out of order.
• dist_quantiles can have all NA values without causing unrelated errors
• adjust default quantiles throughout so that they match.
• force layer_residual_quantiles() to always include 0.5.
• Rename recipes:::check_training_set() to recipes:::validate_training_data(), as it changed in recipes 1.1.0.
• A new column name duplicating an existing column name results in an error instead of a random name.

epipredict 0.1

• 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 post-processing
• 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
• add check_enough_train_data() that will error if training data is too small
• added check_enough_train_data() to arx_forecaster()
• layer_residual_quantiles() will now error if any of the residual quantiles are NA
• *_args_list() functions now warn if forecast_date + ahead != target_date
• the predictor argument in arx_forecaster() now defaults to the value of the outcome argument
• arx_fcast_epi_workflow() and arx_class_epi_workflow() now default to trainer = parsnip::logistic_reg() to match their more canned versions.
• add a forecast() method simplify generating forecasts
• refactor bake.epi_recipe() and remove epi_juice().
• Revise compat-purrr to use the r-lang standalone-* version (via {usethis})
• Replaced old version-faithful example in sliding AR & ARX forecasters vignette
• epi_recipe() will now warn when given non-epi_df data
• layer_predict() and predict.epi_workflow() will now appropriately forward ... args intended for predict.model_fit()
• bake.epi_recipe() will now re-infer the geo and time type in case baking the steps has changed the appropriate values
• produce length 0 dist_quantiles()
• add functionality to calculate weighted interval scores for dist_quantiles()
• Add step_epi_slide to produce generic sliding computations over an epi_df
• Add quantile random forests (via {grf}) as a parsnip engine
• Replace epi_keys() with epiprocess::key_colnames(), #352
• More descriptive error messages from arg_is_*(), #287
• Fix bug where fit() drops the epi_workflow class (also error if non-epi_df data is given to epi_recipe()), #363
• Try to retain the epi_df class during baking to the extent possible, #376