Package index • diseasenowcasting

All functions

auto_nowcast(): Automatically select and fit the best nowcasting model
backtest(): Backtest one or more diseasenowcasting models
best_model(): The winning model() object from a nowcast
best_model_name(): Name of the model chosen by auto_nowcast()
best_score(): The scoreboard row for the model auto_nowcast() chose
comparison_scores(): The model-selection scoreboard from auto_nowcast()
covid_colombia: COVID-19 Notifications – Colombia 2020-2023
cumulative_process(): Count-cumulative observation process
custom_delay() experimental: User-defined delay distribution
custom_epidemic(): User-defined epidemic process
default_priors(): Build the default prior bundle for an RTMB nowcast model
lognormal_delay() gamma_delay() generalized_gamma_delay() dirichlet_delay(): Delay distribution for the Bayesian Nowcast
diseasenowcasting_workflows: Native and cross-engine workflows
dn_palette(): diseasenowcasting colour palette
hsgp_epidemic() ar1_epidemic() sir_epidemic(): Epidemic process for the Bayesian Nowcast
extreme_values(): Surprising (extreme) values flagged during the last update()
fit(): Fit a nowcast model with the RTMB engine
fit_check(): Check RTMB optimizer diagnostics for a fitted nowcast
fix_param(): Hard-fix a parameter in a prior bundle (treat as data, drop from estimation)
infer_max_time(): Infer max_time for custom epidemic processes
poisson_likelihood() nb_likelihood(): Likelihood for the Bayesian Nowcast
load_nowcast(): Load a nowcast saved with save_nowcast()
model(): Bayesian Nowcast Model
nowcast(): Fit a nowcast model to censored reporting data
nowcast_diagnostic(): Three-panel diagnostic plot for a fitted nowcast
nowcast_twostage(): Two-stage multiple-imputation nowcast
parameters(): Parameter estimates from a fitted nowcast
prepare_data(): Prepare data for the RTMB nowcast engine
std_normal_prior() normal_prior() cauchy_prior() student_t_prior() double_exponential_prior() flat_prior() positive_flat_prior() half_std_normal_prior() half_normal_prior() half_cauchy_prior() half_student_t_prior() half_double_exponential_prior() gamma_prior() weibull_prior() inv_gamma_prior() lognormal_prior() chi_square_prior() exponential_prior() logistic_prior() beta_prior(): Priors for model parameters
lognormal_revision() gamma_revision() generalized_gamma_revision() dirichlet_revision(): Revision-lag distributions
revision_process(): Revision process: reports that are later confirmed or retracted
sample(): Draw random samples from a prior (or fall back to base::sample())
save_nowcast(): Save a fitted nowcast to disk
selection_metric(): The metric auto_nowcast() used to pick the winner
selection_timings(): Fitting times recorded by auto_nowcast()
summarise_nowcast_matrix(): Quantile-table summary of a pooled nowcast draws matrix
surprise(): Compute surprise scores for new observations
surprise(<list>): Surprise score on a raw fit() result
theme_diseasenowcasting(): ggplot2 theme matching the diseasenowcasting visual identity
validate_custom_delay() experimental: Validate a custom delay distribution for RTMB traceability
validate_custom_epidemic() experimental: Validate a user-defined epidemic process for RTMB traceability