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BayesChange (version 2.3.0)

detect_cp_uni: Detect Change Points on an univariate time series.

Description

Detect Change Points on an univariate time series.

Usage

detect_cp_uni(
  data,
  n_iterations,
  q,
  a = 1,
  b = 1,
  c = 0.1,
  prior_var_phi = 0.1,
  prior_delta_c = 1,
  prior_delta_d = 1,
  print_progress = TRUE,
  user_seed = 1234L
)

Value

Function detect_cp_uni returns a list containing the following components:

  • $orders a matrix where each row corresponds to the output order of the corresponding iteration.

  • time computational time in seconds.

  • $sigma_MCMC traceplot for \(\sigma\).

  • $delta_MCMC traceplot for \(\delta\).

Arguments

data

vector of observations.

n_iterations

number of MCMC iteration.

q

probability of performing a split at each iterations.

a, b, c

parameters of the Normal-Gamma prior for \(\mu\) and \(\lambda\).

prior_var_phi

parameters for the correlation coefficient in the likelihood.

prior_delta_c, prior_delta_d

parameters of the shifted Gamma prior for \(\delta\).

print_progress

If TRUE (default) print the progress bar.

user_seed

seed for random distribution generation.