R-INLA

excursions

Research Resources

latent Gaussian models spatial statistics applied statistics bayesian inference R-INLA Gaussian random fields excursion sets

excursions: A Framework for Probabilistic Excursion Sets and Contour Inference

Mapping risk on networks: A scalable model for spatial point processes earns international recognition

2 min read · Mon, Jul 28 2025

spatial statistics Gaussian processes R-INLA

KAUST Ph.D. student Damilya Saduakhas wins Best Paper Award for a new model mapping accident risk on complex road networks.

Joint longitudinal-survival models using R-INLA

Janet van Niekerk, Research Scientist, Statistics

May 9, 12:00 - 13:00

B9 L2 H1

stochastic smoothing pipeline R-INLA

Joint models have received increasing attention during recent years with extensions into various directions; numerous hazard functions, different association structures, linear and non-linear longitudinal trajectories amongst others. They gained popularity amongst practitioners by the ability to incorporate various data sources. In this talk, we will introduce joint models and provide some conceptual ideas about their use and necessity. Also, we will illustrate how these models can be formulated as Latent Gaussian Models and hence be implemented using R-INLA.