spFFBS

Overview

This package provides the implementation of the Forward Filtering Backward Sampling (FFBS) algorithm with Dynamic Bayesian Predictive Stacking (DYNBPS) integration for multivariate spatiotemporal models, as introduced in “Adaptive Markovian Spatiotemporal Transfer Learning in Multivariate Bayesian Modeling” (Presicce and Banerjee, 2026+). To guarantee the reproducibility of scientific results, in the Markovian-Spatiotemporal-Propagation repository also includes all the scripts of code used for simulations, data analysis, and results presented in the Manuscript and its Supplemental material.

Installation

If installing from CRAN, use the following.

install.packages("spFFBS")

For a quick installation of the development version, run the following command in R. We use the devtools R package to install. Then, check for its presence on your device, otherwise install it:

if (!require(devtools)) {
  install.packages("devtools", dependencies = TRUE)
}

Once you have installed devtools, we can proceed. Let’s install the spFFBS package!

devtools::install_github("lucapresicce/spFFBS")

Usage

Once successfully installed, load the library in R.

library(spFFBS)

Cool! You are ready to start, now you too could perform fast & feasible Bayesian spatiotemporal modeling!

Contacts

Author	Luca Presicce (l.presicce@campus.unimib.it)
Maintainer	Luca Presicce (l.presicce@campus.unimib.it)
Reference	Luca Presicce and Sudipto Banerjee (2026+) “Adaptive Markovian Spatiotemporal Transfer Learning in Multivariate Bayesian Modeling”