Function reference • covidModel

All functions
`bind_boosted_rt()`	Bind Boosting Results to Stable Rt Estimates
`boost_rt()`	Boost Rt Estimates Given Reference Data and Cross-Validation Errors
`bsts_fit()`	Bayesian Structural Time Series: Fit Model
`bsts_season()`	Bayesian Structural Time Series: Seasonal Components
`bsts_trend()`	Bayesian Structural Time Series: Trend Components
`calc_active_cases()`	Calculate Active Cases Based on 14 Day Heuristic
`calibrate_rt()`	Simple Bayesian Estimates of the Time-Varying Reproduction Number
`clean_linelist()`	Remove Incomplete, Illogical, and Missing Data from an Incidence Linelist
`cv_decomposition()`	Cross-Validate Predictions in an STL Decomposition Conditional on Future Data
`cv_linelist_decomposition()`	Cross-Validate the STL Decomposition Step of `prep_linelist()`
`cv_rt()`	Apply Rolling Cross-Validation to Rt Calculations Given Cross-Validated Decomposition Estimates
`deanomalize()`	Replace Anomalies with Expected Values Using the `anomalize` Package
`estimate_activity()`	Estimate Infectious Activity from Incidence and Serial Interval
`estimate_boosted_rt()`	Boosted Bayesian Estimates of the Effective Reproduction Number
`estimate_delay()`	Estimate Reporting Delay Using a Simple Moving Average
`estimate_rt()`	(Boosted) Bayesian Estimates of Effective Reproduction Number
`estimate_unboosted_rt()`	Run the Full Reproduction Number Modeling Pipeline
`expm1_decomposed()`	Convert Decompositions Performed Under a `log1p()` Transform to Linear Scale
`functionalize()`	Integrate a Vector w.r.t. Time
`interpolate()`	Interpolate a Continuous Function of Time `interpolate()` creates a function of time from a time-indexed input object. It powers `functionalize()`. This documentation is unfinished.
`interpolate_vec()`	Convert a Time-Indexed Vector to Function of Time (i.e. x = f(t))
`nowcast_cases()`	Nowcast Cases by Specimen Collection Date
`nowcast_tests()` `nowcast_tests()`	Nowcast
`plot_rt()`	Plot the Results of `estimate_rt()`
`prep_linelist()`	Prepare a Linelist for Rt Estimation
`prep_linelist_decomposition()`	Prepare Linelist for STL Decomposition of Incidence Curve
`reduce_rt_error()`	Combine a List of Rt Errors into a Tibble Grouped by Forecast Horizon
`sample_rt_error()`	Bootstrap Rt Estimates Using Past Errors
`sirv()`	Simulate a SIR Model with Vaccination and Variants
`summarize_rt_error()`	Compute Updated Summary of Rt Smooth
`tidy_rt()`	Tidy an `estimate_R` Object
`validate_decomposition()`	Map a Dataset to an STL Decomposition Using Data Up to a Specific Time Point
`validate_rt()`	Compute Errors for Rt Smoothing
`weight_rt_error()`	Calculate Geometrically Decaying Weights for Rt Sampling

Reference

All functions