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 …

The use of complex statistical models has recently increased substantially in the context of
species distribution behavior. This complexity has made the inferential and predictive …

Analyzing ecological data often requires modeling the autocorrelation created by spatial and
temporal processes. Many seemingly disparate statistical methods used to account for …

Educational attainment is an important social determinant of maternal, newborn, and child
health,–. As a tool for promoting gender equity, it has gained increasing traction in popular …

Understanding the uncertainty in spatial modelling of environmental variables is important
because it provides the end-users with the reliability of the maps. Over the past decades …

Wildfires have become one of the principal environmental problems in the Mediterranean
basin. While fire plays an important role in most terrestrial plant ecosystems, the potential …

As any model for digital soil map** suffers from different types of errors, including
interpolation errors, it is important to quantify the uncertainty associated with the maps …

Understanding spatial physical habitat selection driven by competition and/or predator–prey
interactions of mobile marine species is a fundamental goal of spatial ecology. However …

Residential radon exposure is a serious public health concern, and as such appears in the
recommendations of European Code Against Cancer. The objective of this study was to …

Analyzing spatial data often requires modeling dependencies created by a dynamic spatio-
temporal data generating process. In many applications, a generalized linear mixed model …