#' outbreaker2: main function for reconstructing disease outbreaks

#'

#' The function \code{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.\cr

#'

#' 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

#' \code{vignette("outbreaker2_custom")}.\cr

#'

#' 

#'

#' @export

#'

#' @aliases outbreaker

#'

#' @rdname outbreaker

#'

#' @author Thibaut Jombart (\email{thibautjombart@@gmail.com}).

#'

#' @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.

#'

#' @seealso \code{\link{outbreaker_data}} to process input data, and

#'     \code{\link{create_config}} to process/set up parameters

#'

#' @param data a list of named items containing input data as returned by

#'     \code{\link{outbreaker_data}}.

#'

#' @param config a set of settings as returned by \code{\link{create_config}}.

#'

#' @param likelihoods a set of log-likelihood functions as returned by

#'     \code{\link{custom_likelihoods}}.

#'

#' @param priors a set of log-prior functions as returned by

#'     \code{\link{custom_priors}}.

#'

#' @param moves a set of movement functions as returned by

#'     \code{\link{custom_moves}}.

#' @seealso

#'

#' \itemize{

#'

#' \item \code{\link{outbreaker_data}}: function to process input data.

#'

#' \item \code{\link{create_config}}: function to create default and customise

#' configuration settings.

#'

#' \item \code{\link{custom_priors}}: function to specify customised prior

#' functions.

#'

#' \item \code{\link{custom_likelihoods}}: function to specify customised likelihoods

#' functions.

#'

#' \item \code{\link{custom_moves}}: function to create default and customise movement

#' functions.

#'

#' }

#'

#' @examples

#'

#' ## get data

#' data(fake_outbreak)

#' dat <- fake_outbreak

#'

#' \dontrun{

#' ## 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)

#'

#' }

outbreaker <- function(data = outbreaker_data(),

                       config = create_config(),

                       priors = custom_priors(),

                       likelihoods = custom_likelihoods(),

                       moves = custom_moves()

                       ) {

    ## CHECKS / PROCESS DATA ##

    data <- outbreaker_data(data = data)

    ## CHECK / PROCESS CONFIG ##

    config <- create_config(config, data = data)

    ## ADD CONVOLUTIONS TO DATA ##

    data <- add_convolutions(data = data, config = config)

    ## PROCESS CUSTOM FUNCTIONS FOR PRIORS AND LIKELIHOOD ##

    priors <- custom_priors(priors)

    loglike <- custom_likelihoods(likelihoods)

    ## CREATE AND INITIALIZE MCMC CHAIN ##

    temp <- create_param(data = data, config = config)

    param_store <- temp$store

    param_current <- temp$current

    param_store <- outbreaker_init_mcmc(data, param_current, param_store,

                                        loglike, priors, config)

    ## here we create a list of function for moving parameters

    moves <- bind_moves(moves = moves,

                        config = config,

                        data = data,

                        likelihoods = loglike,

                        priors = priors)

    ## IMPORTS

    ## preliminary run to detect imported cases this relies on a shorter run of

    ## the MCMC, then computing the average 'global influence' (-loglike) of

    ## each data point, identifying outliers (based on fixed threshold) and

    ## marking outliers down as 'imported cases'.

    temp <- outbreaker_find_imports(moves = moves,

                                    data = data,

                                    param_current = param_current,

                                    param_store = param_store,

                                    config = config,

                                    likelihoods = loglike)

    param_current <- temp$param_current

    param_store <- temp$param_store

    config <- temp$config

    ## PERFORM MCMC

    ## procedure is the same as before, with some cases fixed as 'imported'

    param_store <- outbreaker_move(moves = moves,

                                   data = data,

                                   param_current = param_current,

                                   param_store = param_store,

                                   config = config,

                                   likelihoods = loglike,

                                   priors = priors)

    ## SHAPE RESULTS

    ## this takes the chains generated by 'outbreaker_move', stored as a list,

    ## and puts everything back together as a single data.frame; augmented data

    ## stored as vectors (e_g. 'alpha') become numbered columns of the

    ## data.frame (e_g. 'alpha_1', 'alpha_2' etc.)

    out <- outbreaker_mcmc_shape(param_store, data)

    return(out)

}