Results 1 to 10 of about 14,233,615 (186)
Bayesian Spatial Modelling with R-INLA [PDF]
Journal of Statistical Software, 2015 The principles behind the interface to continuous domain spatial models in the R- INLA software package for R are described. The integrated nested Laplace approximation (INLA) approach proposed by Rue, Martino, and Chopin (2009) is a computationally ...
Finn Lindgren, Håvard Rue
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Spatial and temporal modeling of breast cancer mortality in Kansas: An R-INLA approach. [PDF]
PLoS ONEIntroductionBased on Breast Cancer Statistics, 2025, breast cancer is a leading cause of death among women in the United States. Geographic disparities and associated risk factors influence breast cancer mortality over time and across spatial areas ...
Stephanie Colwell +4 more
doaj +3 more sources
Spatial modeling with R‐INLA: A review [PDF]
WIREs Computational Statistics, 2018 Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically‐sized datasets from scratch is time‐consuming, and if changes are made to the model, there is little guarantee that the code performs well.
Bakka, Haakon +8 more
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Spatial and spatio-temporal models with R-INLA [PDF]
Spatial and Spatio-temporal Epidemiology, 2013 During the last three decades, Bayesian methods have developed greatly in the field of epidemiology. Their main challenge focusses around computation, but the advent of Markov Chain Monte Carlo methods (MCMC) and in particular of the WinBUGS software has opened the doors of Bayesian modelling to the wide research community. However model complexity and
Blangiardo, Marta +3 more
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Competing risks joint models using R-INLA [PDF]
Statistical Modelling, 2020 The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models has largely been neglected, especially from a practitioner's point of view. In the relevant works on competing risks joint models, the assumptions of a Gaussian linear longitudinal series and proportional cause ...
Van Niekerk, Janet +2 more
openaire +4 more sources
Spatio‐temporal data integration for species distribution modelling in R‐INLA
Methods in Ecology and EvolutionSpecies distribution modelling is a highly used tool for understanding and predicting biodiversity change, and recent work has emphasised the importance of understanding how species distributions change over both time and space.
Fiona M. Seaton +2 more
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Addressing spatial confounding in geostatistical regression models: An R‐INLA approach
Methods in Ecology and EvolutionSpatial confounding, which has been studied extensively in recent years, can explain inconsistencies between results obtained by regression models with and without spatial modelling.
Jérémy Lamouroux +4 more
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Revista Latinoamericana de Población, 2020 El trabajo busca modelar la distribución de la tasa de incidencia acumulada de COVID-19 en los municipios de México a través del ajuste de tres modelos lineales generalizados (en competencia) con efectos espaciales y temporales y función de enlace ...
Gerardo Núñez Medina
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Estimating Spatial Econometrics Models with Integrated Nested Laplace Approximation
Mathematics, 2021 The integrated nested Laplace approximation (INLA) provides a fast and effective method for marginal inference in Bayesian hierarchical models. This methodology has been implemented in the R-INLA package which permits INLA to be used from within R ...
Virgilio Gómez-Rubio +2 more
doaj +1 more source
New Frontiers in Bayesian Modeling Using the INLA Package in R
Journal of Statistical Software, 2021 The INLA package provides a tool for computationally efficient Bayesian modeling and inference for various widely used models, more formally the class of latent Gaussian models.
Janet Van Niekerk +3 more
doaj +1 more source