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
growth_rate_from_prevalence() experimental: Estimate relative growth rate from estimated prevalence
growth_rate_from_proportion() experimental: Estimate relative growth rate from modelled proportion
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,
linelist(): Coerce an object to a ggoutbreak compatible case linelist.
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
timeseries(): Coerce an object to a ggoutbreak compatible time series dataframe
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_fixed_ip(): Generate a simple discrete infectivity profile from a gamma distribution
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 line-list of cases 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() experimental: Plot a timeseries of disease prevalence
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.time_period() seq(<time_period>) is.time_period() date_to_time() time_to_date(): Time period S3 class methods
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
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
set_defaults() with_defaults() set_default_start() set_default_unit(): Set or reset the default origin and unit for time periods
time_aggregate(): Aggregate time series data preserving the time series
time_summarise(): Summarise data from a line list to a time-series of counts.
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
dcgamma(): Density: gamma distribution constrained to have mean > sd
dgamma2(): The Gamma Distribution
dlnorm2(): The Log Normal Distribution
dlogitnorm(): Logit-normal distribution
dlogitnorm2(): Logit-normal distribution
dnbinom2(): The Negative Binomial Distribution
dnull(): Null distributions always returns NA
dwedge(): Wedge distribution
pbeta2(): The Beta Distribution
pcgamma(): Cumulative probability: gamma distribution constrained to have mean > sd
pgamma2(): The Gamma Distribution
plnorm2(): The Log Normal Distribution
plogitnorm(): Logit-normal distribution
plogitnorm2(): Logit-normal distribution
pnbinom2(): The Negative Binomial Distribution
pnull(): Null distributions always returns NA
pwedge(): Wedge distribution
qbeta2(): The Beta Distribution
qcgamma(): Quantile: gamma distribution constrained to have mean > sd
qgamma2(): The Gamma Distribution
qlnorm2(): The Log Normal Distribution
qlogitnorm(): Logit-normal distribution
qlogitnorm2(): Logit-normal distribution
qnbinom2(): The Negative Binomial Distribution
qnull(): Null distributions always returns NA
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
rcgamma(): Sampling: gamma distribution constrained to have mean > sd
rdiscgamma(): Random count data from a discrete gamma distribution
rexpgrowth(): Randomly sample incident times in an exponentially growing process
rexpgrowthI0(): Randomly sample incident times in an exponentially growing process with initial case load
rgamma2(): The Gamma Distribution
rlnorm2(): The Log Normal Distribution
rlogitnorm(): Logit-normal distribution
rlogitnorm2(): Logit-normal distribution
rnbinom2(): The Negative Binomial Distribution
rnull(): Null distributions always returns NA
rwedge(): Wedge distribution
wedge: Wedge distribution

Data Documentation

Others

reband_discrete(): Reband any discrete distribution
vcov_from_residuals(): Estimate Parametric VCOV Matrix from Residuals