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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.
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).
Area-to-Area Kriging
Point-scale variogram deconvolution from irregular/regular spatial support according to Goovaerts, P., (2008)
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.
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/>.