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.

Mapping Fisheries Data and Spatial Analysis Tools

This software has evolved from fisheries research conducted at the Pacific Biological Station (PBS) in 'Nanaimo', British Columbia, Canada. It extends the R language to include two-dimensional plotting features similar to those commonly available in a Geographic Information System (GIS). Embedded C code speeds algorithms from computational geometry, such as finding polygons that contain specified point events or converting between longitude-latitude and Universal Transverse Mercator (UTM) coordinates. Additionally, we include 'C++' code developed by Angus Johnson for the 'Clipper' library, data for a global shoreline, and other data sets in the public domain. Under the user's R library directory '.libPaths()', specifically in './PBSmapping/doc', a complete user's guide is offered and should be consulted to use package functions effectively.

Calculate Sun Position, Sunrise, Sunset, Solar Noon and Twilight

Provides a set of convenient functions for calculating sun-related information, including the sun's position (elevation and azimuth), and the times of sunrise, sunset, solar noon, and twilight for any given geographical location on Earth. These calculations are based on equations provided by the National Oceanic & Atmospheric Administration (NOAA) < https://gml.noaa.gov/grad/solcalc/calcdetails.html> as described in "Astronomical Algorithms" by Jean Meeus (1991, ISBN: 978-0-943396-35-4).

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.

Maps, Data and Methods Related to Guerry (1833) "Moral Statistics of France"

Maps of France in 1830, multivariate datasets from A.-M. Guerry and others, and statistical and graphic methods related to Guerry's "Moral Statistics of France". The goal is to facilitate the exploration and development of statistical and graphic methods for multivariate data in a geospatial context of historical interest.

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>.

Convert Statistical Objects into Tidy Tibbles

Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.

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.