Main Gibbs Sampler for EXNEX Survival Models — cpp_exnex

Implements Gibbs sampling for Bayesian EXNEX right-censored log-normal survival models with optional covariates.

Usage

cpp_exnex_gibbs(time, event, group, X, priors, iter, warmup, chains)

time: Vector of observed follow-up times (n-vector, all > 0)
event: Vector of event indicators (n-vector, 0 or 1)
group: Vector of group assignments (n-vector, integers 1 to K)
X: Matrix of covariates (n x P). Can be empty (n x 0) if no covariates.
priors: Optional named list of prior hyperparameters. Supported fields: a_sigma, b_sigma, a_tau, b_tau (inverse-Gamma shape and scale), p_mix (EXNEX mixture weight), m_mu, v_mu (exchangeable mean prior), m_nex, v_nex (nonexchangeable component), v_beta (variance of the regression-coefficient prior). p_mix, m_nex, and v_nex each accept either a scalar, replicated across baskets, or a numeric vector of length K with one value per basket, matching the basket-specific notation \(p_{\mathrm{exch},j}\), \(m_{0j}\), \(v_{0j}\) of the model. Absent fields keep the defaults; unknown fields are ignored.
iter: Total number of MCMC iterations
warmup: Number of iterations to discard
chains: Number of independent chains to run

List containing posterior draws, priors, metadata, and diagnostics