This is the public repository for the r-inla project
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Updated
Sep 5, 2024 - HTML
This is the public repository for the r-inla project
worked R examples
Models of the expansion of the black death and cholera outbreaks
Survival analysis in health economic evaluation Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation. survHE can fit a large range of survival models using both a frequentist approach (by calling the R package flexsurv) and a Bayesian perspective.
An R package for Bayesian structural equation modeling using INLA
In this work, we stratify malaria cases across Malawi to sub-district level, quantify spatio-temporal patterns and examined the effects of climate, environment, and malaria intervention.
Temporal Integrative Genomics Analysis in R
Fast approximate Bayesian inference of HIV indicators using PCA adaptive Gauss-Hermite quadrature
This repository contains example code to estimate spatially smoothed small area estimates of firearm suicide mortality rates across counties in the contiguous United States.
An R package with useful functions to diagnose INLA models
Repository to the R package nowcaster, nowcasting with INLA
A Shiny App for Spatial Modeling. It allows the users to perform geostatistical models, LGCP models, preferential models and joint models with mixture independent-dependent samplings (which we will refer as mixture models). This is done under the Bayesian paradigm and by means of the INLA approach.
This repository contains code to perform the wildlife density modelling approach outlined in Houldcroft et al. (2024).
Code and data from the "Spatial and Spatio-temporal Bayesian Models with R-INLA" book
An introductory course to the INLA-software
Approximate inference of latent non-Gaussian models
Supplementary material and reproducible research files for article “A joint Bayesian framework for missing data and measurement error using integrated nested Laplace approximations” by Emma Skarstein, Sara Martino and Stefanie Muff.
Online version of Dynamic Time Series Models using R-INLA: An Applied Perspective. Ravishanker, Raman, Soyer, 2022. Also includes R codes and data sets from the book.
Spatial Regression of Epilepsy Admissions in Australia
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