@article{JSSv081i14, title={Local Likelihood Estimation for Covariance Functions with Spatially-Varying Parameters: The convoSPAT Package for R}, volume={81}, url={https://www.jstatsoft.org/index.php/jss/article/view/v081i14}, doi={10.18637/jss.v081.i14}, abstract={In spite of the interest in and appeal of convolution-based approaches for nonstationary spatial modeling, off-the-shelf software for model fitting does not as of yet exist. Convolution-based models are highly flexible yet notoriously difficult to fit, even with relatively small data sets. The general lack of pre-packaged options for model fitting makes it difficult to compare new methodology in nonstationary modeling with other existing methods, and as a result most new models are simply compared to stationary models. Using a convolution-based approach, we present a new nonstationary covariance function for spatial Gaussian process models that allows for efficient computing in two ways: first, by representing the spatially-varying parameters via a discrete mixture or "mixture component" model, and second, by estimating the mixture component parameters through a local likelihood approach. In order to make computations for a convolutionbased nonstationary spatial model readily available, this paper also presents and describes the convoSPAT package for R. The nonstationary model is fit to both a synthetic data set and a real data application involving annual precipitation to demonstrate the capabilities of the package.}, number={14}, journal={Journal of Statistical Software}, author={Risser, Mark D. and Calder, Catherine A.}, year={2017}, pages={1–32} }