Changelog • BFI

BFI v3.2.0

CRAN release: 2026-09-26

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.

CRAN release: 2025-05-24

The MAP.estimation function has been revised, and corresponding updates have been made to its documentation and examples.

CRAN release: 2025-05-19

In this release, the package has been updated to support both observational and randomized trial data for estimating treatment effects using Bayesian Federated ‘Causal’ Inference.

In this release, the package has been updated to support a (semi-parametric) Cox model with an ‘unspecified’ baseline hazard, maximizing partial log-likelihood.
The documentation and examples have been updated.

CRAN release: 2024-07-04

This is a bugfix release to resolve a minor bug in the optim function entries related to the gaussian family.

The package now supports survival data analysis, adding comprehensive tools for time-to-event modeling alongside existing GLM functionalities.
The documentation and examples have been updated.

CRAN release: 2024-04-27

The package has been updated to address a few NOTEs identified during the CRAN submission.

The functions inv.prior.cov() and bfi() were adapted when there is a center specific variable.
The function summary() was updated to be used in the case of stratification.
The manual pdf was updated in the case of center specific variable.
Henceforth the BFI package can be called from Python.
A vignette was also added to the package for calling BFI from Python.

In this release, the package website was built using the pkgdown package.
Most of the functions were adapted with extra arguments in the case of stratification.
The manual pdf was also updated.

In this release, the package was updated so that the outputs of inv.prior.cov() and MAP.estimation() (for different centers) have the same dimensions when intercept=FALSE to be used in bfi().
Moreover, one argument in bfi(), i.e., const_var, is added to the package to handle the constant variables.
This is a bugfix release to resolve one minor bug as well.

In this release, the summary function (a S3 method for class bfi) was added to the package.
A package pdf manual was created for the package.

In this release, the package was updated with several arguments, e.g., an intercept should be fitted or not.
A vignette is added to the package as well.

This is a bugfix release to resolve one minor bug, and add two functions related to building Gamma matrix.
Moreover, the package now handles the categorical covariates with more than two levels.