Package index • ggoutbreak

Incidence, Growth Rate and Reproduction Number estimators

doubling_time(): Doubling time from growth rate

gam_delayed_reporting(): Delayed GAM reporting model function generator

gam_knots(): Derive a set of knot points for a GAM from data

gam_nb_model_fn(): Default GAM count negative binomial model.

gam_poisson_model_fn(): Default GAM count model.

growth_rate_from_incidence(): Estimate growth rate from modelled incidence

infer_population(): Infers a daily baseline population for a timeseries

infer_prevalence() experimental: Infer the prevalence of disease from incidence estimates and population size.

infer_rate_ratio() experimental: Calculate a risk ratio from incidence

infer_risk_ratio() experimental: Calculate a normalised risk ratio from proportions

inv_wallinga_lipsitch(): Calculate a growth rate from a reproduction number and an infectivity profile,

multinomial_nnet_model(): Multinomial time-series model.

normalise_count(): Calculate a normalised count per capita

normalise_incidence(): Calculate a normalised incidence rate per capita

poisson_gam_model(): GAM poisson time-series model

poisson_glm_model(): Poisson time-series model.

poisson_locfit_model(): Poisson time-series model.

proportion_glm_model(): Binomial time-series model.

proportion_locfit_model(): A binomial proportion estimate and associated exponential growth rate

rescale_model(): Rescale a timeseries in the temporal dimension

rt_cori(): Reproduction number estimate using the Cori method

rt_epiestim(): EpiEstim reproduction number wrapper function

rt_from_growth_rate(): Wallinga-Lipsitch reproduction number from growth rates

rt_from_incidence(): Reproduction number from modelled incidence

rt_from_renewal(): Reproduction number from renewal equation applied to modelled incidence using statistical re-sampling

rt_incidence_reference_implementation(): Reference implementation of the Rt from modelled incidence algorithm

rt_incidence_timeseries_implementation(): Time series implementation of the Rt from modelled incidence algorithm

wallinga_lipsitch(): Calculate the reproduction number from a growth rate estimate and an infectivity profile

Delay distributions and Infectivity profiles

format_ip(): Print a summary of an infectivity profile

make_empirical_ip(): Recover a long format infectivity profile from an EpiEstim style matrix

make_gamma_ip(): Make an infectivity profile from published data

make_posterior_ip(): Make an infectivity profile from posterior samples

make_resampled_ip(): Re-sample an empirical IP distribution direct from data

omega_matrix(): Generate a infectivity profile matrix from a long format

summarise_ip(): Generate a single infectivity profile from multiple bootstraps

Visualisations

breaks_log1p(): A scales breaks generator for log1p scales

geom_events(): Add time series event markers to a time series plot.

integer_breaks(): Strictly integer breaks for continuous scale

logit_trans(): logit scale

plot_cases(): Plot a raw case counts as a histogram

plot_counts(): Plot a raw case count timeseries

plot_growth_phase(): Plot an incidence or proportion versus growth phase diagram

plot_growth_rate(): Growth rate timeseries diagram

plot_incidence(): Plot an incidence timeseries

plot_ip(): Plot an infectivity profile

plot_multinomial(): Plot a multinomial proportions model

plot_prevalence(): Plot a proportions timeseries

plot_proportion(): Plot a proportions timeseries

plot_proportions_data(): Plot a raw case count proportion timeseries

plot_rt(): Reproduction number timeseries diagram

scale_x_log1p(): A log1p x scale

scale_x_logit(): A logit x scale

scale_y_log1p(): A log1p y scale

scale_y_logit(): A logit y scale

Time series functions

as.Date(<time_period>) as.POSIXct(<time_period>): Convert time period to dates

as.time_period() c(<time_period>) `[`(<time_period>) `[<-`(<time_period>) `[[`(<time_period>) `[[<-`(<time_period>) seq(<time_period>) is.time_period() print(<time_period>): Convert to a time period class

cut_date(): Places a set of dates within a regular time series

date_seq(<Date>): Expand a date vector to the full range of possible dates

date_seq(): Create the full sequence of values in a vector

date_seq(<numeric>): Create the full sequence of values in a vector

date_seq(<time_period>): Expand a time_period vector to the full range of possible times

date_to_time(): Convert a set of dates to numeric timepoints

fdmy(): Format date as dmy

is.Date(): Check whether vector is a date

julian(<time_period>): Extract Parts of a POSIXt or Date Object

labels(<time_period>): Label a time period

max_date(): The maximum of a set of dates

min_date(): The minimum of a set of dates

months(<time_period>): Extract Parts of a POSIXt or Date Object

quarters(<time_period>): Extract Parts of a POSIXt or Date Object

time_aggregate(): Aggregate time series data preserving the time series

time_summarise(): Summarise data from a line list to a time-series of counts.

time_to_date(): Convert a set of time points to dates

weekdays(<time_period>): Extract Parts of a POSIXt or Date Object

Simulation and testing functions

cfg_beta_prob_rng(): Generate a random probability based on features of the simulation

cfg_gamma_ip_fn(): Get a IP generating function from time varying mean and SD of a gamma function

cfg_ip_sampler_rng(): Randomly sample from an empirical distribution

cfg_linear_fn(): Linear function from dataframe

cfg_step_fn(): Step function from dataframe

cfg_transition_fn(): Sample from a multinomial transition matrix

cfg_weekly_gamma_rng(): Weekly delay function with day of week effect

cfg_weekly_ip_fn(): Weekly convolution distribution function

cfg_weekly_proportion_rng(): Random probability function with day of week effect

quantify_lag(): Identify estimate lags in a model

score_estimate(): Calculate scoring statistics from predictions.

sim_apply_ascertainment(): Apply a ascertainment bias to the observed case counts.

sim_apply_delay(): Apply delay distribution to count or linelist data

sim_branching_process(): Generate a line list from a branching process model parametrised by reproduction number

sim_convolution(): Apply a time varying probability and convolution to count data

sim_delay(): Apply a time-varying probability and delay function to linelist data

sim_delayed_observation(): Apply a right censoring to count data.

sim_events(): Extract the events dataframe from a simulation output

sim_geom_function(): The principal input function to a ggoutbreak simulation as a ggplot2 layer.

sim_multinomial(): Generate a multinomial outbreak defined by per class growth rates and a poisson model

sim_poisson_Rt_model(): Generate an outbreak case count series defined by Reproduction number using a poisson model.

sim_poisson_model(): Generate an outbreak case count series defined by growth rates using a poisson model.

sim_seir_model(): SEIR model with time-varying transmission parameter

sim_summarise_linelist(): Summarise a line list

sim_test_data(): Generate a simple time-series of cases based on a growth rate step function

Reparameterised statistical distributions

dbeta2(): The Beta Distribution

dgamma2(): The Gamma Distribution

dlnorm2(): The Log Normal Distribution

dnbinom2(): The Negative Binomial Distribution

dwedge(): Wedge distribution

pbeta2(): The Beta Distribution

pgamma2(): The Gamma Distribution

plnorm2(): The Log Normal Distribution

pnbinom2(): The Negative Binomial Distribution

pwedge(): Wedge distribution

qbeta2(): The Beta Distribution

qgamma2(): The Gamma Distribution

qlnorm2(): The Log Normal Distribution

qnbinom2(): The Negative Binomial Distribution

qwedge(): Wedge distribution

rbern(): A random Bernoulli sample as a logical value

rbeta2(): The Beta Distribution

rcategorical(): Sampling from the multinomial equivalent of the Bernoulli distribution

rdiscgamma(): Random count data from a discrete gamma distribution

reparam-dist: Re-parametrised distributions

rgamma2(): The Gamma Distribution

rlnorm2(): The Log Normal Distribution

rnbinom2(): The Negative Binomial Distribution

rwedge(): Wedge distribution

wedge: Wedge distribution

Data Documentation

covid_ip: A COVID-19 infectivity profile based on an empirical resampling approach

covid_test_sensitivity: Test sensitivity of PCR tests

covid_viral_shedding: The COVID-19 viral shedding duration

du_serial_interval_ip: The Du empirical serial interval dataset

england_consensus_growth_rate: The SPI-M-O England consensus growth rate

england_consensus_rt: The SPI-M-O England consensus reproduction number

england_covid: Daily COVID-19 case counts by age group in England

england_covid_pcr_positivity: England COVID-19 PCR test positivity

england_covid_proportion_age_stratified(): The England COVID-19 poisson model dataset

england_covid_poisson_age_stratified(): The England COVID-19 age stratified poisson model dataset

england_covid_proportion_age_stratified: The England COVID-19 age stratified proportion model dataset

england_covid_test_positives: Weekly England COVID test positives by age group including testing effort

england_demographics: England demographics

england_events: Key dated in the COVID-19 response in England

england_nhs_app: NHS COVID-19 app data

england_ons_infection_survey: The england_ons_infection_survey dataset

england_variants: Counts of COVID-19 variants

ganyani_ip: A COVID-19 infectivity profile based on an Ganyani et al 2020

ganyani_ip_2: A COVID-19 infectivity profile based on an Ganyani et al 2020

germany_covid: Weekly COVID-19 case counts by age group in Germany

germany_demographics: Germany demographics

test_bpm: An example of the linelist output of the branching process model simulation

test_delayed_observation: The delayed observation dataset

test_ip: A test infectivity profile generated from a set of discretised gamma distributions with parameters mean 5 (95% CI 4-6) and sd 2 (95% CI 1.5-2.5).

test_poisson_growth_rate: A simulation dataset determined by a step function of growth rates. This is useful for demonstrating growth rate estimators.

test_poisson_rt(): An example of the linelist output of the poisson model simulation with defined $R_t$

test_poisson_rt_2class: The test_poisson_rt_2class dataset

test_poisson_rt_smooth: Output of a poisson model simulation with a smooth function for $R_t$ defined as R(t) = e^(sin(t/80*pi)^4-0.25)). This is a relatively unchallenging test data set that should not pose a problem for smooth estimators.

test_serial: A serial interval estimated from simulated data

test_ts: A test time series dataset, containing no statistical noise.

Others

reband_discrete(): Reband any discrete distribution

vcov_from_residuals(): Estimate Parametric VCOV Matrix from Residuals