Function reference • spimalot

All functions
`spim_add_forecast()`	Add forecasts to combined fit object
`spim_add_par()` `spim_add_par_beta()`	Add parameter
`spim_beta_mult_schools()`	Calculate multiplicative beta for schools
`spim_beta_mult_seasonality()`	Calculate multiplicative beta due to seasonality
`spim_calc_seasonality()`	Seasonality
`spim_calculate_doses()`	Calculate doses given out from simulation
`spim_check_model_type()`	Check a model type
`spim_check_region()`	Validate and expand regions
`spim_check_sircovid_model()`	Check a model type
`spim_combined_load()`	Load combined fits
`spim_combined_load_multiregion()`	Load multiregion fits as a combined fit object
`spim_control()`	High-level control
`spim_control_cores()`	Return the number of cores
`spim_data()`	Organise input data
`spim_extract_alos()`	Extract average length of hospital stay
`spim_extract_variants_rt()`	Extract multivariant Rt
`spim_fit_pars_load()`	Load parameters
`spim_fit_process()`	Process a fit for `lancelot` model
`spim_fit_run()`	Run fit
`spim_mtp_age_vaccine_outputs()`	MTP simulation outputs by age and vaccination class
`spim_mtp_population()`	Generate MTP population from combined
`spim_mtp_summary_to_template()`	Port MTP outcomes to template
`spim_multivariant_rt_plot()`	Create multivariant Rt plot
`spim_pars_check_beta_date()`	Validate vector of beta dates
`spim_pars_pmcmc_load()`	Load a set of parameters for the pmcmc
`spim_pars_pmcmc_save()`	Write out parameters
`spim_particle_filter()`	Construct a particle filter
`spim_plot_Rt()`	Plot Rt
`spim_plot_alos()`	Plot average length of stay over time
`spim_plot_check_doses()`	Check doses given out from simulation
`spim_plot_check_rt()`	Check Rt from simulation
`spim_plot_check_state()`	Check states from simulation
`spim_plot_check_state_by_age()`	Check states by age from simulation
`spim_plot_check_total_doses()`	Check total doses given out from simulation
`spim_plot_check_uptake()`	Check dose uptake from simulation
`spim_plot_cumulative_attack_rate()`	Plot cumulative attack rate
`spim_plot_daily_infections()`	Check daily infections from simulation
`spim_plot_effective_susceptible()`	Plot effective susceptibles
`spim_plot_fit_forecasts()` `spim_plot_fit_traces()` `spim_plot_fit_traces_separate()` `spim_plot_fit_posteriors()`	Plots for fit
`spim_plot_forest()`	Forest plot
`spim_plot_ifr_t()`	Plot IFR over time
`spim_plot_incidence()`	Plot incidence
`spim_plot_infection_status()`	Plot infection status
`spim_plot_infections_per_strain()`	Plot infections per strain
`spim_plot_log_traj_by_age()`	Plot log trajectories by age
`spim_plot_ons()`	Plot ONS
`spim_plot_pillar2_cases()`	Plot Pillar 2 cases
`spim_plot_pillar2_positivity()`	Plot Pillar 2 positivity
`spim_plot_prop_susceptible()`	Plot proportion susceptible
`spim_plot_react()`	Plot REACT
`spim_plot_rt_dist()`	Plot Rt distribution
`spim_plot_seasonality()`	Plot seasonality over time
`spim_plot_seeding_date()`	Create plot for estimated seeding date
`spim_plot_serology()`	Plot serology
`spim_plot_trajectories()`	Plot trajectories
`spim_plot_trajectories_by_age()`	Plot trajectories by age
`spim_plot_vaccine_figures()`	Create plot for vaccine paper figure 1
`spim_plot_vaccine_status()`	Plot vaccine status
`spim_plot_variant()`	Plot variant
`spim_plot_voc_proportion()`	Create plot for VOC proportion
`spim_plot_voc_range()`	Plot relative strain transmissibility ranges
`spim_population()`	Get population from combined
`spim_prepare_aggregated_data()`	Prepare aggregated data for plotting
`spim_prop_infected()`	Get proportion infected from combined
`spim_region_name()`	Convert region names
`spim_rejuvenatoR()`	Find daily little r values from big R
`spim_restart_initial()`	Create restart initial conditions
`spim_restart_initial_state()`	Load restart data
`spim_restart_join_parent()`	Join parent and restart fits
`spim_restart_pars()`	Create restart parameters
`spim_rrq_controller()`	Find rrq controller
`spim_scenario_cols()`	Return accessible scenario colours
`spim_simulate_add_all_deaths()`	Combine all death trajectories
`spim_simulate_add_diagnoses_admitted()`	Combine diagnoses and admitted trajectories
`spim_simulate_add_trajectory_incidence()`	Calculate incidence trajectories for a simulated object
`spim_simulate_args()`	Create simulation parameters
`spim_simulate_combine_trajectories()`	Combine regional simulated trajectories
`spim_simulate_complete_doses()`	Calculate final doses date
`spim_simulate_control()`	Create control parameters
`spim_simulate_control_output()`	Create output control for simulations
`spim_simulate_create_summary()`	Summarise simulations over particles
`spim_simulate_local()`	Run simulations locally
`spim_simulate_parameter_grid()`	Prepare parameter update list
`spim_simulate_prepare()`	Prepare for simulation
`spim_simulate_process_output()`	Process simulation output
`spim_simulate_remove_dates_to()`	Remove simulations up to given date
`spim_simulate_reset_cumulative_states()`	Reset cumulative states to zero at start of simulation
`spim_simulate_rrq()`	Run simulations with rrq
`spim_simulate_set_beta_step()`	Add beta_step into control
`spim_simulate_simplify_rt()`	Simplify simulation object by moving Rt in with trajectories
`spim_simulate_tidy_states()`	Create tidy (long) dataframe of simulated results
`spim_simulation_predictors()`	Return predictive simulation variables
`spim_simulation_shaps()`	Calculate SHAPs over predicted states
`spim_summary_nowcast()` `spim_summary_time_series()` `spim_summary_write()`	Create summaries
`spim_vaccination_data()`	Prepare vaccination data
`tidy_state_one()`	Create tidy (long) dataframe of simulated results

Reference

All functions