Function reference • serosolver

All functions
`add_measurement_shifts()`	Add measurement shifts to predictions
`add_noise()`	Add noise
`bb_mean()`	Beta binomial mean
`bb_var()`	Beta binomial variance
`calculate_infection_history_statistics()`	Get posterior information infection histories
`calc_phi_probs()`	Calculate FOI log probability
`calc_phi_probs_indiv()`	Calculate FOI log probability vector
`calc_phi_probs_spline()`	Calculate FOI from spline - INACTIVE
`check_attack_rates()`	Checks the attack_rates supplied in simulate_data
`check_data()`	Checks the entries of data used in serosolver
`check_inf_hist()`	Check infection history matrix
`check_par_tab()`	Check par_tab for simulate_data
`check_proposals()`	Checks if the multivarite proposal is being used with the FOI proposal
`create_age_mask()`	Create age mask
`create_cross_reactivity_vector()`	Convert melted antigenic map to cross reactivity
`create_posterior_func()`	Posterior function pointer
`create_prior_mu()`	Create strain specific bias prior
`create_prob_shifts()`	Measurement shift creation
`create_strain_mask()`	Create strain mask
`cs1_chains_real`	MCMC chains case study 1, real
`cs1_chains_real_b`	MCMC chains case study 1, real, for sim
`cs1_chains_sim`	MCMC chains case study 1, sim
`cs2_chains_real`	MCMC chains case study 2, real
`cs2_chains_real_b`	MCMC chains case study 2, real, for sim
`cs2_chains_sim`	MCMC chains case study 2, sim
`db()`	Beta prior on an infection
`dbb()`	Beta binomial density
`dbb_prior()`	Beta binomial prior used here (no choose constant)
`density_beta_binom()`	Beta binomial density
`describe_priors()`	Describe infection history priors
`estimate_mode()`	Estimate vector mode
`example_antigenic_map`	Example antigenic map
`example_inf_chain`	Example MCMC chain for infection histories
`example_inf_hist`	Example infection history matrix
`example_par_tab`	Example parameter table
`example_theta_chain`	Example MCMC chain for kinetics parameters
`example_titre_data`	Example titre data
`expand_summary_infChain()`	Expand sparse infection history matrix
`expand_summary_inf_chain()`	Expands default MCMC saved inf_chain
`find_bb_2()`	Beta binomial parameter match
`find_beta_prior_mode()`	Find Beta distribution parameters with mode
`find_beta_prior_with_mean()`	Find beta parameters with maximum variance
`find_beta_prior_with_mean_var()`	Find beta parameters for mean and variance
`find_prior_alpha_beta()`	Beta binomial parameters
`fit_beta_prior()`	Fit beta distribution to MCMC output
`fit_normal_prior()`	Fit normal distribution to MCMC output
`fromUnitScale()`	Convert from unit scale to original scale
`generate_antigenic_map()`	Generate antigenic map
`generate_antigenic_map_flexible()`	Generate antigenic map, flexible
`generate_ar_annual()`	Generates attack rates from an SIR model with fixed beta/gamma, specified final attack rate and the number of time "buckets" to solve over ie. buckets=12 returns attack rates for 12 time periods
`generate_cumulative_inf_plots()`	Plot cumulative and per time posterior infection probability densities
`generate_phis()`	Generate FOI phis - INACTIVE
`generate_quantiles()`	Formatted quantiles
`generate_start_tab()`	Generate starting parameter table
`gen_spline_y()`	Generates a spline for `generate_phis` - INACTIVE
`get_best_pars()`	Best pars
`get_DOBs()`	Get DOBs
`get_index_pars()`	Index pars
`get_n_alive()`	Get number alive
`get_n_alive_group()`	Get number alive by location
`get_titre_predictions()`	Generate titre credible intervals
`get_total_number_infections()`	Get total number of infections
`hist_rbb()`	Beta binomial histogram
`infection_history_prior()`	Beta binomial infection history prior
`infection_history_symmetric()`	Brute force infection history proposal
`inf_hist_prop_prior_v2_and_v4()`	Infection history gibbs proposal
`inf_hist_prop_prior_v3()`	Fast infection history proposal function
`inf_hist_swap()`	Swap infection history years
`inf_hist_swap_phi()`	Swap infection history years with phi term
`inf_mat_prior()`	Infection history prior R
`inf_mat_prior_cpp()`	Marginal prior probability (p(Z)) of a particular infection history matrix single prior Prior is independent contribution from each year
`inf_mat_prior_cpp_vector()`	Marginal prior probability (p(Z)) of a particular infection history matrix vector prior Prior is independent contribution from each year, but each year has its own alpha and beta
`inf_mat_prior_group_cpp()`	Marginal prior probability (p(Z)) of infection history matrix with groups Prior is independent contribution from each year and group
`inf_mat_prior_group_cpp_vector()`	Marginal prior probability (p(Z)) of a particular infection history matrix vector prior by group Prior is independent contribution from each time, but each time has its own alpha and beta
`inf_mat_prior_total_group_cpp()`	Marginal prior probability (p(Z)) of a particular infection history matrix total prior Prior here is on the total number of infections across all individuals and times
`likelihood_func_fast()`	Fast observation error function Calculate the probability of a set of observed titres given a corresponding set of predicted titres. FAST IMPLEMENTATION
`load_antigenic_map_file()`	Read in antigenic_map
`load_infection_chains()`	Load MCMC chains for infection histories
`load_mcmc_chains()`	Combine theta and infection history chains
`load_start_tab()`	Read in start_tab
`load_theta_chains()`	Load MCMC chains for theta
`load_titre_dat()`	Read in titre_dat
`melt_antigenic_coords()`	Create useable antigenic map
`mvr_proposal()`	Multivariate proposal function
`pad_alphas_and_betas()`	Pad par_tab with alpha and betas
`pad_inf_chain()`	Pad infection history chain
`pbb()`	Beta binomial distribution function
`plot_attack_rates()`	Plot historical attack rates
`plot_attack_rates_monthly()`	Plot historical attack rates monthly
`plot_data()`	Useful plot for looking at simulated data
`plot_infection_histories()`	Plots infection histories and titre model fits
`plot_infection_history_chains_indiv()`	Plot MCMC trace for infections per individual
`plot_infection_history_chains_time()`	Plot MCMC trace for infections per year
`plot_number_infections()`	Plot point range number infections per individual
`plot_posteriors_infhist()`	Plot inferred posteriors infection histories
`plot_posteriors_theta()`	Plot inferred posteriors theta
`plot_samples_distances()`	Plot time between serum samples
`plot_total_number_infections()`	Total number of infections
`prob_mus()`	Prior probability of strain specific boosting
`prob_shifts()`	Prior on measurement shifts
`protect()`	Protect function
`qbb()`	Beta binomial quantile function
`rbb()`	Beta binomial random generation
`rm_scale()`	Robins and Monro scaler, thanks to Michael White
`serosolver()`	Adaptive Metropolis-within-Gibbs/Metropolis Hastings Random Walk Algorithm.
`r_likelihood()`	Likelihood function given data
`save_infection_history_to_disk()`	Write given infection history to disk
`scaletuning()`	Scale step sizes
`setup_infection_histories()`	Propose initial infection histories
`setup_infection_histories_old()`	Propose initial infection histories - OLD VERSION
`setup_infection_histories_titre()`	Propose initial infection histories based on titres - use this!
`setup_infection_histories_total()`	Initial infection history prior on total
`setup_titredat_for_posterior_func()`	Setup titre data indices
`simulate_attack_rates()`	Simulate attack rates
`simulate_cross_sectional()`	Simulates multiple cohorts in a cross-sectional framework.
`simulate_data()`	Simulate full data set
`simulate_group()`	Simulate group data
`simulate_individual()`	Simulate individual data
`simulate_individual_faster()`	Simulate individual data quickly
`simulate_infection_histories()`	Simulate infection histories
`subset_nullable_vector()`	Takes a subset of a Nullable NumericVector, but only if it isn't NULL
`sum_buckets()`	Sums a vector based on bucket sizes
`sum_infections_by_group()`	Count infections by group and time
`sum_likelihoods()`	Sum likelihoods into buckets
`titre_data_fast()`	Overall model function, fast implementation
`titre_dependent_boosting_plot()`	Titre dependent boosting relationship
`to.pdf()`	PDF - Rich's function to print to device without potential for bad errors
`to.png()`	PNG - Rich's function to print to device without potential for bad errors
`to.svg()`	SVG - Rich's function to print to device without potential for bad errors
`toUnitScale()`	Convert to unit scale
`univ_proposal()`	MCMC proposal function
`wane_function()`	Function to calculate non-linear waning All additional parameters for the function are declared here

Reference

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