Functions for kriging and point pattern analysis.

Provides a set of spatial accessibility measures from a set of locations 
             (demand) to another set of locations (supply). It aims, among others, 
             to support research on spatial accessibility to health care facilities. 
             Includes the locations and some characteristics of major public hospitals in Greece.

Implements atom-based regression models (ABRM) for analyzing spatially 
    misaligned data. Provides functions for simulating misaligned spatial data, 
    preparing NIMBLE model inputs, running MCMC diagnostics, and comparing different 
    spatial analysis methods including dasymetric mapping. All main functions return
    S3 objects with print(), summary(), and plot() methods for intuitive result exploration.
    Methods are described in Nethery et al. (2023) <doi:10.1101/2023.01.10.23284410>.
    Further methodological details and software implementation are described in Qian et al. (in review).

Blind source separation for multivariate spatial data based on simultaneous/joint diagonalization of (robust) local covariance matrices. This package is an implementation of the methods described in Bachoc, Genton, Nordhausen, Ruiz-Gazen and Virta (2020) <doi:10.1093/biomet/asz079>.

Seamlessly create interactive online catalogues for geospatial data. Items can be mapped as points or areas and retrieved using either a map or a dynamic table with search form and optional column filters.

Functions that compute the spatial covariance matrix for the matern and power classes of spatial models, for data that arise on rectangular units.  This code can also be used for the change of support problem and for spatial data that arise on irregularly shaped regions like counties or zipcodes by laying a fine grid of rectangles and aggregating the integrals in a form of Riemann integration.

The aim of the spatial downscaling is to increase the spatial resolution of the gridded geospatial input data. This package contains two deep learning based spatial downscaling methods, super-resolution deep residual network (SRDRN) (Wang et al., 2021 <doi:10.1029/2020WR029308>) and UNet (Ronneberger et al., 2015 <doi:10.1007/978-3-319-24574-4_28>), along with a statistical baseline method bias correction and spatial disaggregation (Wood et al., 2004 <doi:10.1023/B:CLIM.0000013685.99609.9e>). The SRDRN and UNet methods are implemented to optionally account for cyclical temporal patterns in case of spatio-temporal data. For more details of the methods, see Sipilä et al. (2025) <doi:10.48550/arXiv.2512.13753>.