R package inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions. Facilitates spatial and general latent Gaussian modeling using integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>). Additionally, extends the GAM-like model class to more general nonlinear predictor expressions, and implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. Model components are specified with general inputs and mapping methods to the latent variables, and the predictors are specified via general R expressions, with separate expressions for each observation likelihood model in multi-likelihood models. A prediction method based on fast Monte Carlo sampling allows posterior prediction of general expressions of the latent variables. Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) <doi:10.1111/2041-210X.13168>.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Francisco Palmí-Perales, Virgilio Gómez-Rubio, Miguel A. Martinez-Beneito: Bayesian Multivariate Spatial Models for Lattice Data with INLA (2021) not zbMATH
- Maximillian H.K. Hesselbarth: shar: An R package to analyze species-habitat associations using point pattern analysis (2021) not zbMATH
- Watson, Joe; Joy, Ruth; Tollit, Dominic; Thornton, Sheila J.; Auger-Méthé, Marie: Estimating animal utilization distributions from multiple data types: a joint spatiotemporal point process framework (2021)
- Ho, Lam Si Tung; Nguyen, Binh T.; Dinh, Vu; Nguyen, Duy: Posterior concentration and fast convergence rates for generalized Bayesian learning (2020)