Package index • EpiEstim

All functions
`DiscrSI()`	Function to ensure compatibility with EpiEstim versions <2.0
`EstimateR()`	Function to ensure compatibility with EpiEstim versions <2.0
`Flu1918`	Data on the 1918 H1N1 influenza pandemic in Baltimore.
`Flu2009`	Data on the 2009 H1N1 influenza pandemic in a school in Pennsylvania.
`Measles1861`	Data on the 1861 measles epidemic in Hagelloch, Germany.
`MockRotavirus`	Mock data on a rotavirus epidemic.
`OverallInfectivity()`	Function to ensure compatibility with EpiEstim versions <2.0
`SARS2003`	Data on the 2003 SARS epidemic in Hong Kong.
`Smallpox1972`	Data on the 1972 smallpox epidemic in Kosovo
`WT()`	Function to ensure compatibility with EpiEstim versions <2.0
`aggregate_inc()`	Aggregating daily incidence to longer time windows
`backimpute_I()`	Impute unobserved generations of infection
`check_cdt_samples_convergence()`	Checking convergence of an MCMC chain by using the Gelman-Rubin algorithm
`coarse2estim()`	Link coarseDataTools and EpiEstim
`compute_lambda()`	Compute the overall infectivity
`compute_si_cutoff()`	Index before which at most a given probability mass is captured
`compute_t_min()`	Compute the smallest index at which joint estimation should start
`covid_deaths_2020_uk`	Data on the 2020-2022 SARS-CoV-2 epidemic in the UK.
`default_mcmc_controls()`	Set default for MCMC control
`default_priors()`	Set default for Gamma priors
`discr_si()`	Discretized Generation Time Distribution Assuming A Shifted Gamma Distribution
`draw_R()`	Draw R from marginal posterior distribution
`draw_epsilon()`	Draw epsilon from marginal posterior distribution
`estimate_R()`	Estimated Instantaneous Reproduction Number
`estimate_R_agg()`	Estimated Instantaneous Reproduction Number from coarsely aggregated data
`estimate_R_plots()`	Wrapper for plot.estimate_R
`estimate_advantage()`	Jointly estimate the instantaneous reproduction number for a reference pathogen/strain/variant and the relative transmissibility of a "new" pathogen/strain/variant
`first_nonzero_incid()`	Get the first day of non-zero incidence across all variants and locations.
`flu_2009_NYC_school`	Data on the 2009 H1N1 influenza pandemic in a school in New York city
`get_shape_R_flat()`	Precompute shape of posterior distribution for R
`get_shape_epsilon()`	Precompute shape of posterior distribution for epsilon
`init_mcmc_params()`	init_mcmc_params Finds clever starting points for the MCMC to be used to estimate the serial interval, e.g. when using option `si_from_data` in `estimate_R`
`make_config()`	Set and check parameter settings of estimate_R
`make_mcmc_control()`	make_mcmc_control Creates a list of mcmc control parameters to be used in `config$mcmc_control`, where `config` is an argument of the `estimate_R` function. This is used to configure the MCMC chain used to estimate the serial interval within `estimate_R` (with method "si_from_data").
`mers_2014_15`	Data on Middle East Respiratory Syndrome (MERS) in Saudi Arabia.
`overall_infectivity()`	Overall Infectivity Due To Previously Infected Individuals
`plot(<estimate_R>)`	Plot outputs of estimate_r
`process_I_multivariant()`	Process incidence input for multivariant analyses with estimate_advantage
`sample_posterior_R()`	sample from the posterior R distribution
`wallinga_teunis()`	Estimation of the case reproduction number using the Wallinga and Teunis method

Reference

All functions