title: Implementing a different Kernel for 2D Kernel Density Estimation in R
body: <p>I'm looking for some help understanding how to implement a 2-dimensional kernel density method, with a isotropic variance, and a bivariate normal kernel, kind of, but instead of using the typical distance, because the data is on the surface of the earth, I need to use a great-circle distance. </p>
  
  <p>I'd like to replicate this in R, but I can't figure out how to use a distance metric other than the simple euclidean distance for any of the built in estimators, and since it uses a complex method with convolutions to add the kernels. Does anyone have a way to program an arbitrary kernel?</p>