Package index • epipredict

Simple forecasters

Complete forecasters that produce reasonable baselines

arx_forecaster(): Direct autoregressive forecaster with covariates

cdc_baseline_forecaster(): Predict the future with the most recent value

flatline_forecaster(): Predict the future with today's value

arx_classifier(): Direct autoregressive classifier with covariates

Forecaster modifications

Constructors to modify forecaster arguments and utilities to produce epi_workflow objects

arx_args_list(): ARX forecaster argument constructor

arx_class_args_list(): ARX classifier argument constructor

cdc_baseline_args_list(): CDC baseline forecaster argument constructor

flatline_args_list(): Flatline forecaster argument constructor

arx_class_epi_workflow(): Create a template arx_classifier workflow

arx_fcast_epi_workflow(): Create a template arx_forecaster workflow

Helper functions for Hub submission

flusight_hub_formatter(): Format predictions for submission to FluSight forecast Hub

Parsnip engines

Prediction methods not available elsewhere

quantile_reg(): Quantile regression

smooth_quantile_reg(): Smooth quantile regression

grf_quantiles: Random quantile forests via grf

Custom panel data forecasting workflows

epi_recipe(): Create a epi_recipe for preprocessing data

epi_workflow(): Create an epi_workflow

add_epi_recipe() remove_epi_recipe() update_epi_recipe(): Add an epi_recipe to a workflow

adjust_epi_recipe(): Adjust a step in an epi_workflow or epi_recipe

Add_model() Remove_model() Update_model() add_model() remove_model() update_model(): Add a model to an epi_workflow

predict(<epi_workflow>): Predict from an epi_workflow

fit(<epi_workflow>): Fit an epi_workflow object

augment(<epi_workflow>): Augment data with predictions

forecast(<epi_workflow>): Produce a forecast from an epi workflow

Epi recipe preprocessing steps

step_epi_naomit(): Unified NA omission wrapper function for recipes

step_epi_lag() step_epi_ahead(): Create a shifted predictor

step_epi_slide(): Calculate a rolling window transformation

step_growth_rate(): Calculate a growth rate

step_lag_difference(): Calculate a lagged difference

step_population_scaling(): Convert raw scale predictions to per-capita

step_training_window(): Limits the size of the training window to the most recent observations

Epi recipe verification checks

check_enough_train_data(): Check the dataset contains enough data points.

Forecast postprocessing

Create a series of postprocessing operations

frosting(): Create frosting for postprocessing predictions

add_frosting() remove_frosting() update_frosting(): Add frosting to a workflow

adjust_frosting(): Adjust a layer in an epi_workflow or frosting

apply_frosting(): Apply postprocessing to a fitted workflow

extract_frosting(): Extract the frosting object from a workflow

get_test_data(): Get test data for prediction based on longest lag period

tidy(<frosting>): Tidy the result of a frosting object

Frosting layers

add_layer(): Add layer to a frosting object

extract_layers() is_layer() validate_layer() detect_layer(): Extract, validate, or detect layers of frosting

layer_add_forecast_date(): Postprocessing step to add the forecast date

layer_add_target_date(): Postprocessing step to add the target date

layer_cdc_flatline_quantiles(): CDC Flatline Forecast Quantiles

layer_naomit(): Omit NAs from predictions or other columns

layer_point_from_distn(): Converts distributional forecasts to point forecasts

layer_population_scaling(): Convert per-capita predictions to raw scale

layer_predict(): Prediction layer for postprocessing

layer_predictive_distn(): Returns predictive distributions

layer_quantile_distn(): Returns predictive quantiles

layer_residual_quantiles(): Creates predictions based on residual quantiles

layer_threshold(): Lower and upper thresholds for predicted values

layer_unnest(): Unnest prediction list-cols

update(<layer>): Update post-processing layer

slather(): Spread a layer of frosting on a fitted workflow

Automatic forecast visualization

autoplot(<epi_workflow>) autoplot(<canned_epipred>): Automatically plot an epi_workflow or canned_epipred object

Utilities for quantile distribution processing

dist_quantiles(): A distribution parameterized by a set of quantiles

extrapolate_quantiles(): Summarize a distribution with a set of quantiles

nested_quantiles(): Turn a vector of quantile distributions into a list-col

weighted_interval_score(): Compute weighted interval score

pivot_quantiles_longer(): Pivot columns containing dist_quantile longer

pivot_quantiles_wider(): Pivot columns containing dist_quantile wider

Other utilities

clean_f_name(): Create short function names

Included datasets

case_death_rate_subset: Subset of JHU daily state cases and deaths

state_census: State population data

grad_employ_subset: Subset of Statistics Canada median employment income for postsecondary graduates