Supporting spherical coordinates?

[mike-lawrence]

I'm currently using mgcv::gam with spherical data where I can formulate a model as y ~ s(latitude,longitude,bs='sos',dim=2). Since, as far as I understand, the main thing that differentiates this from an isotropic smooth in cartesian coordinates is the use of great-circle distance rather than euclidean, it strikes me as something that should be fairly straightforward to implement?

[jtimonen]

lgpr does support terms with two continuous variables, so you can only define a model like
lgp(y ~ gp(latitude) + gp(longitude) in this case. Therefore you don't have a kernel with Euclidean distance in a 2D space, just a kernel which is a sum of two one-dimensional kernels. It is possible for the user to transform coordinates to a different system and use a model like lgp(y ~ gp(transformed_coord1) + gp(transformed_coord2) but the model will still be additive for the two coordinates.