research
∙
05/13/2023
Indexing and Partitioning the Spatial Linear Model for Large Data Sets
We consider four main goals when fitting spatial linear models: 1) estim...
share
research
∙
05/04/2023
Marginal Inference for Hierarchical Generalized Linear Mixed Models with Patterned Covariance Matrices Using the Laplace Approximation
Using a hierarchical construction, we develop methods for a wide and fle...
research
∙
02/15/2022
SSNbayes: An R package for Bayesian spatio-temporal modelling on stream networks
Spatio-temporal models are widely used in many research areas from ecolo...
research
∙
03/05/2021
Bayesian spatio-temporal models for stream networks
Spatio-temporal models are widely used in many research areas including ...
research
∙
05/03/2020
A Linear Mixed Model Formulation for Spatio-Temporal Random Processes with Computational Advances for the Separable and Product-Sum Covariances
We describe spatio-temporal random processes using linear mixed models. ...
research
∙
12/02/2019
SSNdesign – an R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks
Streams and rivers are biodiverse and provide valuable ecosystem service...
research
∙
12/26/2018
Comparing Spatial Regression to Random Forests for Large Environmental Data Sets
Environmental data may be "large" due to number of records, number of co...
research
∙
10/19/2017
