SP-OS-MFRF Spatial prediction and optimal sampling for multivariate functional random fields

Spatial functional data analysis is an alternative approach to spatio-temporal modeling when the curves are time series varying spatially. Specifically, functional geostatistics allows to carry out optimal spatial prediction of the whole curve at unsampled sites. Along with the developments in functional geostatistics, the framework of the optimal spatial sampling designs has been extended. This package is concerned with functional kriging, functional cokriging and optimal sampling designs for spatial prediction of functional data. The methodologies are illustrated by an application to a dataset taken from the meteorological and air quality networks of Mexico city.