TY - JOUR AU - Risser, Mark D. AU - Calder, Catherine A. PY - 2017/11/13 Y2 - 2024/03/29 TI - Local Likelihood Estimation for Covariance Functions with Spatially-Varying Parameters: The convoSPAT Package for R JF - Journal of Statistical Software JA - J. Stat. Soft. VL - 81 IS - 14 SE - Articles DO - 10.18637/jss.v081.i14 UR - https://www.jstatsoft.org/index.php/jss/article/view/v081i14 SP - 1 - 32 AB - 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. ER -