DetectCpObj: DetectCpObj class constructor

Description

Constructor for the DetectCpObj class. This class stores the output of the Bayesian change–point detection algorithm, including MCMC traces, allocation orders, and computational information.

Usage

DetectCpObj(
  data = NULL,
  n_iterations = NULL,
  n_burnin = NULL,
  orders = NULL,
  time = NULL,
  entropy_MCMC = NULL,
  lkl_MCMC = NULL,
  phi_MCMC = NULL,
  sigma_MCMC = NULL,
  delta_MCMC = NULL,
  I0_MCMC = NULL,
  kernel_ts = NULL,
  kernel_epi = NULL,
  univariate_ts = NULL
)

Arguments

data: A vector or matrix containing the observed time series.
n_iterations: Total number of MCMC iterations.
n_burnin: Number of burn-in iterations to discard.
orders: A matrix where each row corresponds to the latent block assignment (order) of the time indices at each MCMC iteration.
time: Computational time in seconds.
entropy_MCMC: A coda::mcmc object containing MCMC samples of the entropy measure.
lkl_MCMC: A coda::mcmc object containing MCMC samples of the log-likelihood.
phi_MCMC: A coda::mcmc object containing MCMC draws for \(\gamma\).
sigma_MCMC: A coda::mcmc object containing MCMC draws for \(\sigma\).
delta_MCMC: A coda::mcmc object containing MCMC draws for \(\delta\).
I0_MCMC: A coda::mcmc object containing MCMC draws for \(I_0\).
kernel_ts: Logical; TRUE if the model for time series data is used.
kernel_epi: Logical; TRUE if the epidemic diffusion model is used.
univariate_ts: Logical; TRUE if the time series is univariate, FALSE otherwise.