The function outbreaker is the main function of the package. It runs processes various inputs (data, configuration settings, custom priors, likelihoods and movement functions) and explores the space of plausible transmission trees of a densely sampled outbreaks.

outbreaker(data = outbreaker_data(), config = create_config(),
  priors = custom_priors(), likelihoods = custom_likelihoods(),
  moves = custom_moves())

Arguments

data

a list of named items containing input data as returned by outbreaker_data

config

a set of settings as returned by create_config

priors

a set of log-prior functions as returned by custom_priors

likelihoods

a set of log-likelihood functions as returned by custom_likelihoods

moves

a set of movement functions as returned by custom_moves

Details

The emphasis of 'outbreaker2' is on modularity, which enables customisation of priors, likelihoods and even movements of parameters and augmented data by the user. This the dedicated vignette on this topic vignette("outbreaker2_custom").

References

Jombart T, Cori A, Didelot X, Cauchemez S, Fraser C and Ferguson N (2014). Bayesian reconstruction of disease outbreaks by combining epidemiologic and genomic data. PLoS Computational Biology.

See also

outbreaker_data to process input data, and create_config to process/set up parameters

Examples

## get data data(fake_outbreak) dat <- fake_outbreak if (FALSE) { ## run outbreaker out <- outbreaker(data = list(dna = dat$dna, dates = dat$onset, w_dens = dat$w), config = list(n_iter = 2e4, sample_every = 200)) plot(out) as.data.frame(out) ## run outbreaker, no DNA sequences out2 <- outbreaker(data = list(dates = dat$onset, w_dens = w), config = list(n_iter = 2e4, sample_every = 200)) plot(out2) as.data.frame(out2) }