R: Bayesian Dynamic Borrowing with Flexible Baseline Hazard Function

# Bayesian Dynamic Borrowing with Flexible Baseline Hazard Function

## Documentation for package ‘BayesFBHborrow’ version 1.0.1

- DESCRIPTION file.

## Help Pages

.beta.MH.RW.glm | Beta MH RW sampler from freq PEM fit
.beta_MH_MALA | Proposal beta with a Metropolis Adjusted Langevin (MALA)
.beta_MH_NR | Newton Raphson MH move
.beta_MH_RW | Beta Metropolis-Hastings Random walk move
.beta_mom | Mean for MALA using derivative for beta proposal
.beta_mom.NR.fun | First and second derivative of target for mode and variance of proposal
.birth_move | Birth move in RJMCMC
.dataframe_fun | Create data.frame for piecewise exponential models
.death_move | Death move in RJMCMC
.glmFit | Fit frequentist piecewise exponential model for MLE and information matrix of beta
.ICAR_calc | Calculate covariance matrix in the MVN-ICAR
.input_check | Input checker
.J_RJMCMC | RJMCMC (with Bayesian Borrowing)
.J_RJMCMC_NoBorrow | RJMCMC (without Bayesian Borrowing)
.lambda_0_MH_cp | Lambda_0 MH step, proposal from conditional conjugate posterior
.lambda_0_MH_cp_NoBorrow | Lambda_0 MH step, proposal from conditional conjugate posterior
.lambda_conj_prop | Propose lambda from a gamma conditional conjugate posterior proposal
.lambda_MH_cp | Lambda MH step, proposal from conditional conjugate posterior
.lgamma_ratio | Calculate log gamma ratio for two different parameter values
.llikelihood_ratio_beta | Loglikelihood ratio calculation for beta parameters
.llikelihood_ratio_lambda | Log likelihood for lambda / lambda_0 update
.logsumexp | Computes the logarithmic sum of an exponential
.log_likelihood | Log likelihood function
.lprop.dens.beta.NR | log Gaussian proposal density for Newton Raphson proposal
.lprop_density_beta | Log density of proposal for MALA
.ltau_dprior | Calculate log density tau prior
.mu_update | Calculate mu posterior update
.normalize_prob | Normalize a set of probability to one, using the the log-sum-exp trick
.nu_sigma_update | Calculates nu and sigma2 for the Gaussian Markov random field prior, for a given split point j
.shuffle_split_point_location | Metropolis Hastings step: shuffle the split point locations (with Bayesian borrowing)
.shuffle_split_point_location_NoBorrow | Metropolis Hastings step: shuffle the split point locations (without Bayesian borrowing)
.sigma2_update | Calculate sigma2 posterior update
.tau_update | Sample tau from posterior distribution
BayesFBHborrow | BayesFBHborrow: Run MCMC for a piecewise exponential model
BayesFBHborrow.NoBorrow | Run the MCMC sampler without Bayesian Borrowing
BayesFBHborrow.WBorrow | Run the MCMC sampler with Bayesian Borrowing
coef.BayesFBHborrow | Extract mean posterior values
GibbsMH | S3 generic, calls the correct GibbsMH sampler
GibbsMH.NoBorrow | GibbsMH sampler, without Bayesian Borrowing
GibbsMH.WBorrow | GibbsMH sampler, with Bayesian Borrowing
group_summary | Create group level data
init_lambda_hyperparameters | Initialize lambda hyperparameters
piecewise_exp_cc | Example data, simulated from a piecewise exponential model.
piecewise_exp_hist | Example data, simulated from a piecewise exponential model.
plot.BayesFBHborrow | Plot the MCMC results
plot_hist | Plot histogram from MCMC samples
plot_matrix | Plot smoothed baseline hazards
plot_trace | Plot MCMC trace
summary.BayesFBHborrow | Summarize fixed MCMC results
weibull_cc | Example data, simulated from a Weibull distribution.
weibull_hist | Example data, simulated from a Weibull distribution