Package: BFI 3.2.0

Hassan Pazira

BFI: Bayesian Federated Inference

The Bayesian Federated Inference ('BFI') method combines inference results obtained from local data sets in the separate centers. In this version of the package, the 'BFI' methodology is programmed for linear, logistic and survival regression models. For GLMs, see Jonker, Pazira and Coolen (2024) <doi:10.1002/sim.10072>; for survival models, see Pazira, Massa, Weijers, Coolen and Jonker (2026) <doi:10.1080/02664763.2025.2511932>; and for heterogeneous populations, see Jonker, Pazira and Coolen (2025) <doi:10.1017/rsm.2025.6>.

Authors:Hassan Pazira [aut, cre], Emanuele Massa [aut], Marianne A. Jonker [aut]

BFI_3.2.0.tar.gz
BFI_3.2.0.zip(r-4.7-any)BFI_3.2.0.zip(r-4.6-any)BFI_3.2.0.zip(r-4.5-any)
BFI_3.2.0.tgz(r-4.6-any)BFI_3.2.0.tgz(r-4.5-any)
BFI_3.2.0.tar.gz(r-4.7-any)BFI_3.2.0.tar.gz(r-4.6-any)
BFI_3.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
BFI/json (API)

# Install 'BFI' in R:
install.packages('BFI', repos = c('https://hassanpazira.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/hassanpazira/bfi/issues

Pkgdown/docs site:https://hassanpazira.github.io

Datasets:
  • Nurses - Nurses' stress in different hospitals
  • trauma - Trauma patients from different hospitals

On CRAN:

Conda:

4.78 score 4 stars 9 scripts 552 downloads 14 exports 0 dependencies

Last updated from:3ef074891a. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK154
source / vignettesOK246
linux-releaseOK156
macos-releaseOK158
macos-oldrelOK173
windows-develOK100
windows-releaseOK106
windows-oldrelOK117
wasm-releaseOK116

Exports:A.l.makerb.diagbfihazards.funi.basisinv.prior.covlambda.polyMAP.estimationmodel.makern.parnegloglik.thetaoptim.survivalql.LRTsurv.simulate

Dependencies:

An Introduction to BFI in R
Overview | How to use it? | MAP estimations | BFI estimations | Comparison | References | Contact
Rendered fromBFI.Rmdusingknitr::rmarkdown

Last update: 2026-09-25
Started: 2024-04-20

Calling BFI from Python
Introduction | Google Colab | Python Installation | On macOS | On Ubuntu | On Windows | Installation of Required Modules | Python Script | Importing Python modules and functions | Installing R packages from CRAN and GitHub | Installing the BFI package from GitHub (if necessary) | Applying BFI method to the simulated data | Data Simulation for two Local Centers | MAP Estimates at the Local Centers | Sending the Required Files to Central Server | Central server with R | Central server with Python | BFI at Central Server (using Python) | Datasets included in the BFI package | All code together | Contact
Rendered fromPython.Rmdusingknitr::rmarkdown

Last update: 2026-09-25
Started: 2024-04-20

Using BFI in SAS
Introduction | Accessing SAS Services | Install R and Configuring It with SAS | The RLANG System Option | Install R | Install SAS/IML Interface to R | Using PROC IML and Rsubmit | Installing R packages from CRAN and GitHub in SAS: | Example | Simulate data for two local centers | MAP estimates at the local centers | BFI at central center | Data sets included in the BFI package | Importing the data from R | BFI as a SAS Package | Contact
Rendered fromSAS.Rmdusingknitr::rmarkdown

Last update: 2026-09-25
Started: 2025-05-23