Added S3 methods print.bfi(), coef.bfi(), and vcov.bfi() for objects of class "bfi".
Improved the handling and presentation of Gaussian residual-variance parameters. Posterior covariance matrices returned by vcov() are transformed from the log(sigma2) scale to the original sigma2 scale using the delta method when applicable.
Improved summary.bfi() output for center-specific Gaussian residual variances, including delta-method standard deviations and back-transformed credible intervals.
Corrected the curvature calculation for parametric survival models with exponential, Weibull, Gompertz, and exponentiated-polynomial baseline hazards so that the Gaussian-prior precision matrix is added once to the likelihood curvature.
Corrected the weighted curvature calculation for Cox models with an unspecified baseline hazard.
Improved parameter alignment in bfi(). Local parameter vectors, curvature matrices, and corresponding prior precision matrices with the same named parameters may now have different parameter orders across centers; the parameter order of the first center is used as the reference order.
Extended name-based parameter alignment to exponentiated-polynomial survival models.
Corrected handling of scalar q_ls values for exponentiated-polynomial survival models.
MAP.estimation function has been revised, and corresponding updates have been made to its documentation and examples.optim function entries related to the gaussian family.inv.prior.cov() and bfi() were adapted when there is a center specific variable.summary() was updated to be used in the case of stratification.BFI package can be called from Python.BFI from Python.inv.prior.cov() and MAP.estimation() (for different centers) have the same dimensions when intercept=FALSE to be used in bfi().bfi(), i.e., const_var, is added to the package to handle the constant variables.bfi) was added to the package.