Added an optional formula/data interface to MAP.estimation() and inv.prior.cov(), while retaining the original y/X interface. The statistical estimation and federated aggregation procedures remain unchanged.
Updated S3 class handling to distinguish local and federated BFI objects while preserving compatibility with existing print(), summary(), coef(), and vcov() methods.
Corrected Gaussian regression inference by consistently using the log-residual-variance parameterization for local estimation, curvature calculations, and federated aggregation.
Retained residual-variance estimates on the original scale in the reported coefficients.
Expanded numerical validation and unit tests for Gaussian regression, including curvature and covariance calculations.
Improved numerical stability of binomial regression by using a stable softplus expression for the negative log-likelihood and plogis() for the curvature calculation.
Corrected propensity-score calculations in second-round Gaussian and binomial treatment-effect analyses by applying plogis() directly to the linear predictor. Improved numerical stability of propensity-score calculations in survival models.
Added validation to reject unsupported cross-precision terms between regression coefficients and log-residual variance in Gaussian prior precision matrices.
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.