Estimation of Spatial Autoregressive Models with and without Heteroskedastic Innovations

Functions for fitting Cliff-Ord-type spatial autoregressive models with and without heteroskedastic innovations using Generalized Method of Moments estimation are provided. Some support is available for fitting spatial HAC models, and for fitting with non-spatial endogeneous variables using instrumental variables.

Deprecated Interface Between GRASS Geographical Information System and R

This package will be archived in October 2023 together with 'rgdal'. Deprecated interpreted interface between 'GRASS' geographical information system and R. Transition to new package 'rgrass' < https://grass.osgeo.org/news/2023_06_05_retirement_of_rgrass7/>.

Tools for Handling Spatial Objects

Please note that 'maptools' will be retired during October 2023, plan transition at your earliest convenience (see < https://r-spatial.org/r/2023/05/15/evolution4.html> and earlier blogs for guidance); some functionality will be moved to 'sp'. Set of tools for manipulating geographic data. The package also provides interface wrappers for exchanging spatial objects with packages such as 'PBSmapping', 'spatstat.geom', 'maps', and others.

Area-to-Area Kriging

Point-scale variogram deconvolution from irregular/regular spatial support according to Goovaerts, P., (2008) ; ordinary area-to-area (co)Kriging and area-to-point (co)Kriging.

Bayesian Model Averaging with INLA

Fit Spatial Econometrics models using Bayesian model averaging on models fitted with INLA. The INLA package can be obtained from < https://www.r-inla.org>.

Functions for the Detection of Spatial Clusters of Diseases

A set of functions for the detection of spatial clusters of disease using count data. Bootstrap is used to estimate sampling distributions of statistics.

Create Cartograms with R

Construct continuous and non-contiguous area cartograms.

Core Functionality for Environmental Time Series

Utility functions for working with environmental time series data from known locations. The compact data model is structured as a list with two dataframes. A 'meta' dataframe contains spatial and measuring device metadata associated with deployments at known locations. A 'data' dataframe contains a 'datetime' column followed by columns of measurements associated with each "device-deployment". Ephemerides calculations are based on code originally found in NOAA's "Solar Calculator" < https://gml.noaa.gov/grad/solcalc/>.

Geometric Shadow Calculations

Functions for calculating: (1) shadow height, (2) logical shadow flag, (3) shadow footprint, (4) Sky View Factor and (5) radiation load. Basic required inputs include a polygonal layer of obstacle outlines along with their heights (i.e. "extruded polygons"), sun azimuth and sun elevation. The package also provides functions for related preliminary calculations: breaking polygons into line segments, determining azimuth of line segments, shifting segments by azimuth and distance, constructing the footprint of a line-of-sight between an observer and the sun, and creating a 3D grid covering the surface area of extruded polygons.

Antarctic Spatial Data Manipulation

Loads and creates spatial data, including layers and tools that are relevant to the activities of the Commission for the Conservation of Antarctic Marine Living Resources. Provides two categories of functions: load functions and create functions. Load functions are used to import existing spatial layers from the online CCAMLR GIS such as the ASD boundaries. Create functions are used to create layers from user data such as polygons and grids.