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#' Create an MCMC object to estimate the marginal likelihood
#' using Nested Sampling.
#'
#' This will result in a BEAST run that estimates the marginal
#' likelihood until convergence is achieved.
#' In this context, \code{chain_length} is only an upper bound
#' to the length of that run.
#' @inheritParams default_params_doc
#' @param chain_length upper bound to the length of the MCMC chain
#' @param particle_count number of particles
#' @param sub_chain_length sub-chain length
#' @param epsilon epsilon
#' @return an MCMC object
#' @seealso
#' Use \code{\link{create_mcmc}} to create a regular MCMC.
#' Use \code{\link{create_test_ns_mcmc}} to create an NS MCMC for testing,
#' with, among others, a short MCMC chain length.
#' Use \code{\link{check_ns_mcmc}} to check that an NS MCMC object is valid.
#' @examples
#' if (is_on_ci()) {
#'
#' mcmc <- create_ns_mcmc(
#' chain_length = 1e7,
#' store_every = 1000,
#' particle_count = 1,
#' sub_chain_length = 1000,
#' epsilon = 1e-12
#' )
#'
#' beast2_input_file <- get_beautier_tempfilename()
#' create_beast2_input_file(
#' get_fasta_filename(),
#' beast2_input_file,
#' mcmc = mcmc
#' )
#' file.remove(beast2_input_file)
#'
#' remove_beautier_folder()
#' }
#' @references
#' * [1] Patricio Maturana Russel, Brendon J Brewer, Steffen Klaere,
#' Remco R Bouckaert; Model Selection and Parameter Inference in
#' Phylogenetics Using Nested Sampling, Systematic Biology, 2018,
#' syy050, https://doi.org/10.1093/sysbio/syy050
#' @author Richèl J.C. Bilderbeek
#' @aliases create_ns_mcmc create_mcmc_nested_sampling
#' @export create_ns_mcmc create_mcmc_nested_sampling
create_ns_mcmc <- create_mcmc_nested_sampling <- function(
chain_length = 10000000,
store_every = -1,
pre_burnin = 0,
n_init_attempts = 3,
particle_count = 1,
sub_chain_length = 5000,
epsilon = "1e-12",
tracelog = create_tracelog(),
screenlog = create_screenlog(),
treelog = create_treelog()
) {
# Unsure about 'sample_from_prior' in NS MCMC, Issue #108
mcmc <- create_mcmc(
chain_length = chain_length,
store_every = store_every,
pre_burnin = pre_burnin,
n_init_attempts = n_init_attempts,
sample_from_prior = FALSE,
tracelog = tracelog,
screenlog = screenlog,
treelog = treelog
)
mcmc$particle_count <- particle_count
mcmc$sub_chain_length <- sub_chain_length
mcmc$epsilon <- epsilon
check_nested_sampling_mcmc(mcmc)
mcmc
}
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