Package index • exnexSurv

Fitting

Fit a Bayesian pooling survival model: EXNEX (pooling = "exnex"), complete pooling ("complete"), or no pooling ("none").

Posterior summaries, survival curves, model comparison and simulation.

survival_curves(): Survival curves from an pooling_surv fit
plot(<survival_exnex>): Plot survival curves from an pooling_surv fit
median_survival(): Posterior median survival time
rmst(): Restricted mean survival time (RMST) from an pooling_surv fit
compute_waic(): Pointwise log-likelihood and WAIC from an pooling_surv fit
compare_waic(): Compare multiple pooling_surv fits by WAIC
probability_superiority(): Posterior probability that one group beats another
rank_probabilities(): Posterior rank probabilities for each group
convergence_diagnostics(): Convergence diagnostics (R-hat and effective sample size)
simulate_data(): Simulate basket-trial log-normal survival data

Posterior predictive checks and shrinkage diagnostics for fitted pooling_surv objects.

Summarise, print, and plot fitted pooling_surv objects.

summary(<pooling_surv>): Summarize posterior draws from an pooling_surv fit
print(<pooling_surv>): Print an pooling_surv fit
plot(<pooling_surv>): Plot parameter traces from an pooling_surv fit

These functions are internal and not exported; use with caution.

cpp_exnex_gibbs(): Main Gibbs Sampler for EXNEX Survival Models
pooling_surv_bridge(): Bridge connecting hardhat processed data to the C++ Gibbs Sampler
new_pooling_surv(): Constructor for pooling_surv Objects
.extract_event_vector(): Extract event vector from outcomes
.extract_time_vector(): Extract time vector from outcomes
.make_chain_seeds(): Derive one deterministic seed per chain (restores the global RNG state)
.run_chains_parallel_pooling(): Run all chains concurrently on future workers
.run_single_chain_pooling(): Run a single chain by calling the C++ kernel once