Implementations for papers
To support transparency and reproducibility in our research, the StochProc group provides software implementations accompanying selected publications. Each resource delivers well-documented code in R, Matlab, or C, enabling users to reproduce results, replicate analyses, and apply advanced spatial modeling techniques directly in their own work.
- R package and code to replicate the results in the paper Efficient methods for Gaussian Markov random fields under sparse linear constraints can be found here.
- Matlab code implementing the methods described in the paper "Spatially adaptive covariance tapering" can be downloaded here.
- Matlab and C-code for estimation of spatially dependent vegetation trends, as described in the paper Fast estimation of spatially dependent temporal vegetation trends using Gaussian Markov random fields can be downloaded here.
- Matlab and R code for simulation and estimation of nested SPDE models, as described in the paper Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping can be downloaded here.