CRAN, GitHub TL;DR: {bdlnm} brings Bayesian Distributed Lag Non-Linear Models (B-DLNMs) to R using INLA, allowing to model complex DLNMs, quantify uncertainty, and produce rich visualizations. Background Climate change is increasing exposure to extreme environmental conditions such as heatwaves and air pollution. However, these exposures rarely have immediate effects. For example: A heatwave today may … Continue reading New R Package {bdlnm} Released on CRAN: Bayesian Distributed Lag Non-Linear Models in R via INLANew R Package {bdlnm} Released on CRAN: Bayesian Distributed Lag Non-Linear Models in R via INLA was first posted on April 10, 2026 at 5:14 pm.

R-bloggers

The bdlnm R package, now on CRAN, implements Bayesian Distributed Lag Non-Linear Models (B-DLNMs) using Integrated Nested Laplace Approximation (INLA) as a fast alternative to MCMC. It extends the existing dlnm package to a full Bayesian setting, enabling flexible exposure-lag-response modeling with uncertainty quantification via posterior distributions. A hands-on tutorial demonstrates fitting a temperature-mortality model on London data (2000–2012), estimating the minimum mortality temperature, generating 3D exposure-lag-response surfaces, lag-response curves, and computing attributable fractions. Results show ~17.5% of deaths in the 75+ population were attributable to non-optimal temperatures (~69,178 deaths). Upcoming features include multi-location analyses and spatial B-DLNMs.