The software for spatial econometrics available in the R system for statistical computing is
reviewed. The methods are illustrated in a historical perspective, highlighting the main lines …

The integrated nested Laplace approximation (INLA) is a recent computational method that
can fit Bayesian models in a fraction of the time required by typical Markov chain Monte …

An intermediate-level treatment of Bayesian hierarchical models and their applications, this
book demonstrates the advantages of a Bayesian approach to data sets involving inferences …

Abstract The Integrated Nested Laplace Approximation (INLA) has established itself as a
widely used method for approximate inference on Bayesian hierarchical models which can …

Network morphological analysis has emerged as a tool to quantify street network structures,
providing a nuanced foundation for evaluating their impacts on traffic safety. Yet, there is a …

Tuberculosis is the most common opportunistic infection among HIV/AIDS patients, including
those following Antiretroviral Therapy treatment. The risk of tuberculosis infection is higher in …

The integrated nested Laplace approximation (INLA) for Bayesian inference is an efficient
approach to estimate the posterior marginal distributions of the parameters and latent effects …

Spatial regression models are widely used in numerous areas, including detecting and
predicting traffic volume, air pollution, and housing prices. Unlike conventional regression …

Technical change essentially drives regional social and economic development, and how
technical change influences the regional sustainable development of the ecological …

The dominant spatial econometric model in spatial econometrics is the parametric form,
while in the realistic context, the variables often do not satisfy the assumption of linearity and …