inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

Canonical link: https://rscholar.com/pkg/inlabru

Finn Lindgren • Fabian E. Bachl • Gorilla data import • sampling • Daniel Simpson • Lindesay Scott-Howard • Seaton Andy • bugfixes • Suen Man Ho • responses • Roudier Pierre • Meehan Tim • Reddy Peddinenikalva Niharika • View description and downloads View dependencies GitHub project

2017 Published

0 Citations

13 Authors

20 Revisions

2.15.0 Version

GPL-2 License

Referenced by ⇅	Year
PointedSDMs: Fit Models Derived from Point Processes to Species Distributions using 'inlabru' (Version 2.1.6)	2022
intSDM: Reproducible Integrated Species Distribution Models Across Norway using 'INLA' (Version 2.1.2)	2022
rSPDE: Rational Approximations of Fractional Stochastic Partial Differential Equations (Version 2.6.0)	2019
INLAspacetime: Spatial and Spatio-Temporal Models using 'INLA' (Version 0.1.14)	2023
INLAtools: Functionalities for the 'INLA' Package (Version 0.1.5)	2025
MetricGraph: Random Fields on Metric Graphs (Version 1.6.0)	2023
bmstdr: Bayesian Modeling of Spatio-Temporal Data with R (Version 0.8.2)	2022
clustTMB: Spatio-Temporal Finite Mixture Model using 'TMB' (Version 0.1.0)	2024
excursions: Excursion Sets and Contour Credibility Regions for Random Fields (Version 2.5.11)	2013
ngme2: Linear Latent Non-Gaussian Models with Flexible Distributions (Version 1.0.0)	2026

RPKG Scholar presents a tabulation of an author's contribution in the development of R packages stored in the Comprehensive R Archive Network (CRAN). Within this site, we consider package dependencies (suggests,imports,depends,enhances) as citations because we believe that using one's package to develop another is tantamount to citing the author of the package being imported, suggested or enhanced.