  Gaussian processes (GPs) are well-known tools for modeling dependent data
with applications in spatial statistics, time series analysis, or econometrics.
In this article, we present the R package varycoef that implements estimation,
prediction, and variable selection of linear models with spatially varying
coefficients (SVC) defined by GPs, so called GP-based SVC models. Such models
offer a high degree of flexibility while being relatively easy to interpret.
Using varycoef, we show versatile applications of (spatially) varying
coefficient models on spatial and time series data. This includes model and
coefficient estimation with predictions and variable selection. The package
uses state-of-the-art computational statistics techniques like parallelization,
model-based optimization, and covariance tapering. This allows the user to work
with (S)VC models in a computationally efficient manner, i.e., model estimation
on large data sets is possible in a feasible amount of time.
