# Roger Bivand

#### 32 packages on CRAN

Selected commonly used methods for choosing univariate class intervals for mapping or other graphics purposes.

Set of tools for manipulating geographic data. It includes binary access to 'GSHHG' shoreline files. The package also provides interface wrappers for exchanging spatial objects with packages such as 'PBSmapping', 'spatstat', 'maps', and others.

Provides bindings to the 'Geospatial' Data Abstraction Library ('GDAL') (>= 1.11.4) and access to projection/transformation operations from the 'PROJ' library. Use is made of classes defined in the 'sp' package. Raster and vector map data can be imported into R, and raster and vector 'sp' objects exported. The 'GDAL' and 'PROJ' libraries are external to the package, and, when installing the package from source, must be correctly installed first; it is important that 'GDAL' < 3 be matched with 'PROJ' < 6. From 'rgdal' 1.5-8, installed with to 'GDAL' >=3, 'PROJ' >=6 and 'sp' >= 1.4, coordinate reference systems use 'WKT2_2019' strings, not 'PROJ' strings. 'Windows' and 'macOS' binaries (including 'GDAL', 'PROJ' and their dependencies) are provided on 'CRAN'.

Interface to Geometry Engine - Open Source ('GEOS') using the C 'API' for topology operations on geometries. The 'GEOS' library is external to the package, and, when installing the package from source, must be correctly installed first. Windows and Mac Intel OS X binaries are provided on 'CRAN'. ('rgeos' >= 0.5-1): Up to and including 'GEOS' 3.7.1, topological operations succeeded with some invalid geometries for which the same operations fail from and including 'GEOS' 3.7.2. The 'checkValidity=' argument defaults and structure have been changed, from default FALSE to integer default '0L' for 'GEOS' < 3.7.2 (no check), '1L' 'GEOS' >= 3.7.2 (check and warn). A value of '2L' is also provided that may be used, assigned globally using 'set_RGEOS_CheckValidity(2L)', or locally using the 'checkValidity=2L' argument, to attempt zero-width buffer repair if invalid geometries are found. The previous default (FALSE, now '0L') is fastest and used for 'GEOS' < 3.7.2, but will not warn users of possible problems before the failure of topological operations that previously succeeded. From 'GEOS' 3.8.0, repair of geometries may also be attempted using 'gMakeValid()', which may, however, return a collection of geometries of different types.

Interpreted interface between 'GRASS' 7 geographical information system and R, based on starting R from within the 'GRASS' 'GIS' environment, or running free-standing R in a temporary 'GRASS' location; the package provides facilities for using all 'GRASS' commands from the R command line. This package may not be used for 'GRASS' 6, for which 'spgrass6' should be used.

A collection of all the estimation functions for spatial cross-sectional models (on lattice/areal data using spatial weights matrices) contained up to now in 'spdep', 'sphet' and 'spse'. These model fitting functions include maximum likelihood methods for cross-sectional models proposed by 'Cliff' and 'Ord' (1973, ISBN:0850860369) and (1981, ISBN:0850860814), fitting methods initially described by 'Ord' (1975) <doi:10.1080/01621459.1975.10480272>. The models are further described by 'Anselin' (1988) <doi:10.1007/978-94-015-7799-1>. Spatial two stage least squares and spatial general method of moment models initially proposed by 'Kelejian' and 'Prucha' (1998) <doi:10.1023/A:1007707430416> and (1999) <doi:10.1111/1468-2354.00027> are provided. Impact methods and MCMC fitting methods proposed by 'LeSage' and 'Pace' (2009) <doi:10.1201/9781420064254> are implemented for the family of cross-sectional spatial regression models. Methods for fitting the log determinant term in maximum likelihood and MCMC fitting are compared by 'Bivand et al.' (2013) <doi:10.1111/gean.12008>, and model fitting methods by 'Bivand' and 'Piras' (2015) <doi:10.18637/jss.v063.i18>; both of these articles include extensive lists of references. 'spatialreg' >= 1.1-* correspond to 'spdep' >= 1.1-1, in which the model fitting functions are deprecated and pass through to 'spatialreg', but will mask those in 'spatialreg'. From versions 1.2-*, the functions will be made defunct in 'spdep'.

A collection of functions to create spatial weights matrix objects from polygon 'contiguities', from point patterns by distance and tessellations, for summarizing these objects, and for permitting their use in spatial data analysis, including regional aggregation by minimum spanning tree; a collection of tests for spatial 'autocorrelation', including global 'Morans I' and 'Gearys C' proposed by 'Cliff' and 'Ord' (1973, ISBN: 0850860369) and (1981, ISBN: 0850860814), 'Hubert/Mantel' general cross product statistic, Empirical Bayes estimates and 'Assun<c3><a7><c3><a3>o/Reis' (1999) <doi:10.1002/(SICI)1097-0258(19990830)18:16%3C2147::AID-SIM179%3E3.0.CO;2-I> Index, 'Getis/Ord' G ('Getis' and 'Ord' 1992) <doi:10.1111/j.1538-4632.1992.tb00261.x> and multicoloured join count statistics, 'APLE' ('Li 'et al.' ) <doi:10.1111/j.1538-4632.2007.00708.x>, local 'Moran's I' ('Anselin' 1995) <doi:10.1111/j.1538-4632.1995.tb00338.x> and 'Getis/Ord' G ('Ord' and 'Getis' 1995) <doi:10.1111/j.1538-4632.1995.tb00912.x>, 'saddlepoint' approximations ('Tiefelsdorf' 2002) <doi:10.1111/j.1538-4632.2002.tb01084.x> and exact tests for global and local 'Moran's I' ('Bivand et al.' 2009) <doi:10.1016/j.csda.2008.07.021> and 'LOSH' local indicators of spatial heteroscedasticity ('Ord' and 'Getis') <doi:10.1007/s00168-011-0492-y>. The implementation of most of the measures is described in 'Bivand' and 'Wong' (2018) <doi:10.1007/s11749-018-0599-x>. 'spdep' >= 1.1-1 corresponds to 'spatialreg' >= 1.1-1, in which the model fitting functions are deprecated and pass through to 'spatialreg', but will mask those in 'spatialreg'. From versions 1.2-1, the functions will be made defunct in 'spdep'. For now 'spatialreg' only has functions from 'spdep', where they are shown as deprecated. 'spatialreg' only loads the namespace of 'spdep'; if you attach 'spdep', the same functions in the other package will be masked. Some feed through adequately, others do not.

Interpreted interface between GRASS 6+ geographical information system and R, based on starting R from within the GRASS environment, or running free-standing R in a temporary GRASS location; the package provides facilities for using all GRASS commands from the R command line. This package may not be used for GRASS 7, for which rgrass7 should be used.

Functions for computing geographically weighted regressions are provided, based on work by Chris Brunsdon, Martin Charlton and Stewart Fotheringham.

The Splancs package was written as an enhancement to S-Plus for display and analysis of spatial point pattern data; it has been ported to R and is in "maintenance mode".

Point-scale variogram deconvolution from irregular/regular spatial support according to Goovaerts, P., (2008) <doi: 10.1007/s11004-007-9129-1>; ordinary area-to-area (co)Kriging and area-to-point (co)Kriging.

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.

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.

A framework that provides the methods for quantifying entropy-based local indicator of spatial association (ELSA) that can be used for both continuous and categorical data. In addition, this package offers other methods to measure local indicators of spatial associations (LISA). Furthermore, global spatial structure can be measured using a variogram-like diagram, called entrogram. For more information, please check that paper: Naimi, B., Hamm, N. A., Groen, T. A., Skidmore, A. K., Toxopeus, A. G., & Alibakhshi, S. (2019) <doi:10.1016/j.spasta.2018.10.001>.

Reading and writing data stored by some versions of 'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', and for reading and writing some 'dBase' files.

Processing collections of Earth observation images as on-demand multispectral, multitemporal raster data cubes. Users define cubes by spatiotemporal extent, resolution, and spatial reference system and let 'gdalcubes' automatically apply cropping, reprojection, and resampling using the 'Geospatial Data Abstraction Library' ('GDAL'). Implemented functions on data cubes include reduction over space and time, applying arithmetic expressions on pixel band values, moving window aggregates over time, filtering by space, time, bands, and predicates on pixel values, exporting data cubes as 'netCDF' or 'GeoTIFF' files, and plotting. The package implements lazy evaluation and multithreading. All computational parts are implemented in C++, linking to the 'GDAL', 'netCDF', 'CURL', and 'SQLite' libraries. See Appel and Pebesma (2019) <doi:10.3390/data4030092> for further details.

Geostatistical analysis including variogram-based, likelihood-based and Bayesian methods. Software companion for Diggle and Ribeiro (2007) <doi:10.1007/978-0-387-48536-2>.

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

Bivariate data interpolation on regular and irregular grids, either linear or using splines are the main part of this package. It is intended to provide FOSS replacement functions for the ACM licensed akima::interp and tripack::tri.mesh functions. Currently the piecewise linear interpolation part of akima::interp (and also akima::interpp) is implemented in interp::interp, this corresponds to the call akima::interp(..., linear=TRUE) which is the default setting and covers most of akima::interp use cases in depending packages. A re-implementation of Akimas spline interpolation (akima::interp(..., linear=FALSE)) is currently under development and will complete this package in a later version. Estimators for partial derivatives are already available, these are a prerequisite for the spline interpolation. The basic part is currently a GPLed triangulation algorithm (sweep hull algorithm by David Sinclair) providing the starting point for the piecewise linear interpolator. As side effect this algorithm is also used to provide replacements for the basic functions of the tripack package which also suffer from the ACM restrictions. All functions are designed to be backward compatible with their akima / tripack counterparts.

Model-based linear model trees adjusting for spatial correlation using a simultaneous autoregressive spatial lag, Wagner and Zeileis (2019) <doi:10.1111/geer.12146>.

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.

Reading, writing, manipulating, analyzing and modeling of spatial data. The package implements basic and high-level functions for raster data and for vector data operations such as intersections. See the manual and tutorials on <https://rspatial.org/> to get started.

Support for simple features, a standardized way to encode spatial vector data. Binds to 'GDAL' for reading and writing data, to 'GEOS' for geometrical operations, and to 'PROJ' for projection conversions and datum transformations. Optionally uses the 's2' package for spherical geometry operations on geographic coordinates.

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.

Classes and methods for spatial data; the classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided, e.g. for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc.

Diverse spatial datasets for demonstrating, benchmarking and teaching spatial data analysis. It includes R data of class sf (defined by the package 'sf'), Spatial ('sp'), and nb ('spdep'). Unlike other spatial data packages such as 'rnaturalearth' and 'maps', it also contains data stored in a range of file formats including GeoJSON, ESRI Shapefile and GeoPackage. Some of the datasets are designed to illustrate specific analysis techniques. cycle_hire() and cycle_hire_osm(), for example, is designed to illustrate point pattern analysis techniques.

Methods for spatial data analysis, especially raster data. Methods allow for low-level data manipulation as well as high-level global, local, zonal, and focal computation. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction. Processing of very large files is supported. See the manual and tutorials on <https://rspatial.org/terra/> to get started. The package is very similar to the 'raster' package; but 'terra' is simpler and faster.

Provides low-level access to 'GDAL' functionality for R packages. The aim is to minimize the level of interpretation put on the 'GDAL' facilities, to enable direct use of it for a variety of purposes. 'GDAL' is the 'Geospatial Data Abstraction Library' a translator for raster and vector geospatial data formats that presents a single raster abstract data model and single vector abstract data model to the calling application for all supported formats <http://gdal.org/>. Other available packages 'rgdal' and 'sf' also provide access to the 'GDAL' library, but neither can be used for these lower level tasks, and both do many other tasks.