R-universe search: spatial

r-spatial

spdep:Spatial Dependence: Weighting Schemes, Statistics

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 'Assunção/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', 'Gearys C' ('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 these measures is described in 'Bivand' and 'Wong' (2018) <doi:10.1007/s11749-018-0599-x>, with further extensions in 'Bivand' (2022) <doi:10.1111/gean.12319>. 'Lagrange' multiplier tests for spatial dependence in linear models are provided ('Anselin et al'. 1996) <doi:10.1016/0166-0462(95)02111-6>, as are 'Rao' score tests for hypothesised spatial 'Durbin' models based on linear models ('Koley' and 'Bera' 2023) <doi:10.1080/17421772.2023.2256810>. A local indicators for categorical data (LICD) implementation based on 'Carrer et al.' (2021) <doi:10.1016/j.jas.2020.105306> and 'Bivand et al.' (2017) <doi:10.1016/j.spasta.2017.03.003> was added in 1.3-7. From 'spdep' and 'spatialreg' versions >= 1.2-1, the model fitting functions previously present in this package are defunct in 'spdep' and may be found in 'spatialreg'.

Maintained by Roger Bivand. Last updated 1 months ago.

spatial-autocorrelation spatial-dependence spatial-weights

121.4 match 131 stars 16.59 score 6.0k scripts 106 dependents

edzer

sp:Classes and Methods for Spatial Data

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. From this version, 'rgdal', 'maptools', and 'rgeos' are no longer used at all, see <https://r-spatial.org/r/2023/05/15/evolution4.html> for details.

Maintained by Edzer Pebesma. Last updated 2 months ago.

64.8 match 127 stars 18.63 score 35k scripts 1.3k dependents

r-spatial

spatialreg:Spatial Regression Analysis

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'. 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. A recent review is provided by 'Bivand', 'Millo' and 'Piras' (2021) <doi:10.3390/math9111276>. 'spatialreg' >= 1.1-* corresponded to 'spdep' >= 1.1-1, in which the model fitting functions were deprecated and passed through to 'spatialreg', but masked those in 'spatialreg'. From versions 1.2-*, the functions have been made defunct in 'spdep'. From version 1.3-6, add Anselin-Kelejian (1997) test to `stsls` for residual spatial autocorrelation <doi:10.1177/016001769702000109>.

Maintained by Roger Bivand. Last updated 8 days ago.

bayesian impacts maximum-likelihood spatial-dependence spatial-econometrics spatial-regression openblas

82.5 match 46 stars 12.97 score 916 scripts 24 dependents

spatstat

spatstat.explore:Exploratory Data Analysis for the 'spatstat' Family

Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.

Maintained by Adrian Baddeley. Last updated 9 days ago.

cluster-detection confidence-intervals hypothesis-testing k-function roc-curves scan-statistics significance-testing simulation-envelopes spatial-analysis spatial-data-analysis spatial-sharpening spatial-smoothing spatial-statistics

97.8 match 1 stars 10.18 score 67 scripts 150 dependents

spatstat

spatstat.geom:Geometrical Functionality of the 'spatstat' Family

Defines spatial data types and supports geometrical operations on them. Data types include point patterns, windows (domains), pixel images, line segment patterns, tessellations and hyperframes. Capabilities include creation and manipulation of data (using command line or graphical interaction), plotting, geometrical operations (rotation, shift, rescale, affine transformation), convex hull, discretisation and pixellation, Dirichlet tessellation, Delaunay triangulation, pairwise distances, nearest-neighbour distances, distance transform, morphological operations (erosion, dilation, closing, opening), quadrat counting, geometrical measurement, geometrical covariance, colour maps, calculus on spatial domains, Gaussian blur, level sets of images, transects of images, intersections between objects, minimum distance matching. (Excludes spatial data on a network, which are supported by the package 'spatstat.linnet'.)

Maintained by Adrian Baddeley. Last updated 4 days ago.

classes-and-objects distance-calculation geometry geometry-processing images mensuration plotting point-patterns spatial-data spatial-data-analysis

79.7 match 7 stars 12.14 score 241 scripts 229 dependents

r-spatial

sf:Simple Features for R

Support for simple feature access, a standardized way to encode and analyze spatial vector data. Binds to 'GDAL' <doi:10.5281/zenodo.5884351> for reading and writing data, to 'GEOS' <doi:10.5281/zenodo.11396894> for geometrical operations, and to 'PROJ' <doi:10.5281/zenodo.5884394> for projection conversions and datum transformations. Uses by default the 's2' package for geometry operations on geodetic (long/lat degree) coordinates.

Maintained by Edzer Pebesma. Last updated 2 days ago.

gdal geos proj spatial cpp

37.0 match 1.4k stars 22.44 score 117k scripts 1.2k dependents

stscl

gdverse:Analysis of Spatial Stratified Heterogeneity

Analyzing spatial factors and exploring spatial associations based on the concept of spatial stratified heterogeneity, while also taking into account local spatial dependencies, spatial interpretability, complex spatial interactions, and robust spatial stratification. Additionally, it supports the spatial stratified heterogeneity family established in academic literature.

Maintained by Wenbo Lv. Last updated 2 days ago.

geographical-detector geoinformatics geospatial-analysis spatial-statistics spatial-stratified-heterogeneity cpp

87.6 match 33 stars 9.10 score 41 scripts 2 dependents

luukvdmeer

sfnetworks:Tidy Geospatial Networks

Provides a tidy approach to spatial network analysis, in the form of classes and functions that enable a seamless interaction between the network analysis package 'tidygraph' and the spatial analysis package 'sf'.

Maintained by Lucas van der Meer. Last updated 3 months ago.

geospatial-networks network-analysis rspatial simple-features spatial-analysis spatial-data-science spatial-networks tidygraph tidyverse

82.7 match 372 stars 9.63 score 332 scripts 6 dependents

r-spatial

stars:Spatiotemporal Arrays, Raster and Vector Data Cubes

Reading, manipulating, writing and plotting spatiotemporal arrays (raster and vector data cubes) in 'R', using 'GDAL' bindings provided by 'sf', and 'NetCDF' bindings by 'ncmeta' and 'RNetCDF'.

Maintained by Edzer Pebesma. Last updated 1 months ago.

raster satellite-images spatial

39.0 match 571 stars 18.27 score 7.2k scripts 137 dependents

spatstat

spatstat.model:Parametric Statistical Modelling and Inference for the 'spatstat' Family

Functionality for parametric statistical modelling and inference for spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Supports parametric modelling, formal statistical inference, and model validation. Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots.

Maintained by Adrian Baddeley. Last updated 7 days ago.

analysis-of-variance cluster-process confidence-intervals cox-process determinantal-point-processes gibbs-process influence leverage model-diagnostics neyman-scott parameter-estimation poisson-process spatial-analysis spatial-modelling spatial-point-processes statistical-inference

68.5 match 5 stars 9.09 score 6 scripts 46 dependents

nowosad

spData:Datasets for Spatial Analysis

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 and GeoPackage, but from version 2.3.4, no longer ESRI Shapefile - use GeoPackage instead. 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.

Maintained by Jakub Nowosad. Last updated 3 months ago.

datasets raster sf sp spatial spdep

41.8 match 82 stars 13.23 score 3.4k scripts 116 dependents

ropensci

stplanr:Sustainable Transport Planning

Tools for transport planning with an emphasis on spatial transport data and non-motorized modes. The package was originally developed to support the 'Propensity to Cycle Tool', a publicly available strategic cycle network planning tool (Lovelace et al. 2017) <doi:10.5198/jtlu.2016.862>, but has since been extended to support public transport routing and accessibility analysis (Moreno-Monroy et al. 2017) <doi:10.1016/j.jtrangeo.2017.08.012> and routing with locally hosted routing engines such as 'OSRM' (Lowans et al. 2023) <doi:10.1016/j.enconman.2023.117337>. The main functions are for creating and manipulating geographic "desire lines" from origin-destination (OD) data (building on the 'od' package); calculating routes on the transport network locally and via interfaces to routing services such as <https://cyclestreets.net/> (Desjardins et al. 2021) <doi:10.1007/s11116-021-10197-1>; and calculating route segment attributes such as bearing. The package implements the 'travel flow aggregration' method described in Morgan and Lovelace (2020) <doi:10.1177/2399808320942779> and the 'OD jittering' method described in Lovelace et al. (2022) <doi:10.32866/001c.33873>. Further information on the package's aim and scope can be found in the vignettes and in a paper in the R Journal (Lovelace and Ellison 2018) <doi:10.32614/RJ-2018-053>, and in a paper outlining the landscape of open source software for geographic methods in transport planning (Lovelace, 2021) <doi:10.1007/s10109-020-00342-2>.

Maintained by Robin Lovelace. Last updated 7 months ago.

cycle cycling desire-lines origin-destination peer-reviewed pubic-transport route-network routes routing spatial transport transport-planning transportation walking

44.9 match 427 stars 12.31 score 684 scripts 3 dependents

r-spatial

gstat:Spatial and Spatio-Temporal Geostatistical Modelling, Prediction and Simulation

Variogram modelling; simple, ordinary and universal point or block (co)kriging; spatio-temporal kriging; sequential Gaussian or indicator (co)simulation; variogram and variogram map plotting utility functions; supports sf and stars.

Maintained by Edzer Pebesma. Last updated 4 days ago.

openblas

34.6 match 197 stars 15.71 score 4.8k scripts 58 dependents

spatstat

spatstat:Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests

Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 3000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.

Maintained by Adrian Baddeley. Last updated 7 days ago.

cluster-process cox-point-process gibbs-process kernel-density network-analysis point-process poisson-process spatial-analysis spatial-data spatial-data-analysis spatial-statistics spatstat statistical-methods statistical-models statistical-tests statistics

33.0 match 200 stars 16.25 score 5.5k scripts 40 dependents

rvalavi

blockCV:Spatial and Environmental Blocking for K-Fold and LOO Cross-Validation

Creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments; Investigating and visualising the effective range of spatial autocorrelation in continuous raster covariates and point samples to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019) <doi:10.1111/2041-210X.13107>.

Maintained by Roozbeh Valavi. Last updated 5 months ago.

cross-validation spatial spatial-cross-validation spatial-modelling species-distribution-modelling cpp

50.8 match 113 stars 10.49 score 302 scripts 3 dependents

apache

apache.sedona:R Interface for Apache Sedona

R interface for 'Apache Sedona' based on 'sparklyr' (<https://sedona.apache.org>).

Maintained by Apache Sedona. Last updated 19 hours ago.

cluster-computing geospatial java python scala spatial-analysis spatial-query spatial-sql

49.4 match 2.0k stars 10.72 score 105 scripts

r-spatial

mapview:Interactive Viewing of Spatial Data in R

Quickly and conveniently create interactive visualisations of spatial data with or without background maps. Attributes of displayed features are fully queryable via pop-up windows. Additional functionality includes methods to visualise true- and false-color raster images and bounding boxes.

Maintained by Tim Appelhans. Last updated 3 months ago.

gis leaflet maps spatial visualization web-mapping

36.6 match 526 stars 14.39 score 7.3k scripts 27 dependents

r-spatial

rgee:R Bindings for Calling the 'Earth Engine' API

Earth Engine <https://earthengine.google.com/> client library for R. All of the 'Earth Engine' API classes, modules, and functions are made available. Additional functions implemented include importing (exporting) of Earth Engine spatial objects, extraction of time series, interactive map display, assets management interface, and metadata display. See <https://r-spatial.github.io/rgee/> for further details.

Maintained by Cesar Aybar. Last updated 2 days ago.

earth-engine earthengine google-earth-engine googleearthengine spatial-analysis spatial-data

37.5 match 717 stars 13.77 score 1.9k scripts 3 dependents

rspatial

terra:Spatial Data Analysis

Methods for spatial data analysis with vector (points, lines, polygons) and raster (grid) data. Methods for vector data include geometric operations such as intersect and buffer. Raster methods include local, focal, global, zonal and geometric operations. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on <https://rspatial.org/> to get started. 'terra' replaces the 'raster' package ('terra' can do more, and it is faster and easier to use).

Maintained by Robert J. Hijmans. Last updated 2 days ago.

geospatial raster spatial vector onetbb proj gdal geos cpp

27.9 match 559 stars 17.64 score 17k scripts 855 dependents

cran

spatial:Functions for Kriging and Point Pattern Analysis

Functions for kriging and point pattern analysis.

Maintained by Brian Ripley. Last updated 3 months ago.

71.6 match 6.53 score 134 dependents

connordonegan

geostan:Bayesian Spatial Analysis

For spatial data analysis; provides exploratory spatial analysis tools, spatial regression, spatial econometric, and disease mapping models, model diagnostics, and special methods for inference with small area survey data (e.g., the America Community Survey (ACS)) and censored population health monitoring data. Models are pre-specified using the Stan programming language, a platform for Bayesian inference using Markov chain Monte Carlo (MCMC). References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Donegan (2021) <doi:10.31219/osf.io/3ey65>; Donegan (2022) <doi:10.21105/joss.04716>; Donegan, Chun and Hughes (2020) <doi:10.1016/j.spasta.2020.100450>; Donegan, Chun and Griffith (2021) <doi:10.3390/ijerph18136856>; Morris et al. (2019) <doi:10.1016/j.sste.2019.100301>.

Maintained by Connor Donegan. Last updated 3 months ago.

bayesian bayesian-inference bayesian-statistics epidemiology modeling public-health rspatial spatial stan cpp

52.5 match 80 stars 8.80 score 46 scripts

geodacenter

rgeoda:R Library for Spatial Data Analysis

Provides spatial data analysis functionalities including Exploratory Spatial Data Analysis, Spatial Cluster Detection and Clustering Analysis, Regionalization, etc. based on the C++ source code of 'GeoDa', which is an open-source software tool that serves as an introduction to spatial data analysis. The 'GeoDa' software and its documentation are available at <https://geodacenter.github.io>.

Maintained by Xun Li. Last updated 21 days ago.

dataanalysis geoda geospatial cpp

58.1 match 73 stars 7.85 score 179 scripts 1 dependents

biodiverse

spOccupancy:Single-Species, Multi-Species, and Integrated Spatial Occupancy Models

Fits single-species, multi-species, and integrated non-spatial and spatial occupancy models using Markov Chain Monte Carlo (MCMC). Models are fit using Polya-Gamma data augmentation detailed in Polson, Scott, and Windle (2013) <doi:10.1080/01621459.2013.829001>. Spatial models are fit using either Gaussian processes or Nearest Neighbor Gaussian Processes (NNGP) for large spatial datasets. Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and Banerjee (2022) <doi:10.18637/jss.v103.i05>. Provides functionality for data integration of multiple single-species occupancy data sets using a joint likelihood framework. Details on data integration are given in Miller, Pacifici, Sanderlin, and Reich (2019) <doi:10.1111/2041-210X.13110>. Details on single-species and multi-species models are found in MacKenzie, Nichols, Lachman, Droege, Royle, and Langtimm (2002) <doi:10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2> and Dorazio and Royle <doi:10.1198/016214505000000015>, respectively.

Maintained by Jeffrey Doser. Last updated 1 months ago.

openblas cpp openmp

56.2 match 59 stars 7.31 score 204 scripts

r-tmap

tmap:Thematic Maps

Thematic maps are geographical maps in which spatial data distributions are visualized. This package offers a flexible, layer-based, and easy to use approach to create thematic maps, such as choropleths and bubble maps.

Maintained by Martijn Tennekes. Last updated 1 days ago.

choropleth-maps maps spatial thematic-maps visualisation

24.7 match 879 stars 16.25 score 13k scripts 24 dependents

bioc

Voyager:From geospatial to spatial omics

SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. Univariate methods include univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. Bivariate methods include Lee's L and cross variogram. Multivariate methods include MULTISPATI PCA and multivariate local Geary's C recently developed by Anselin. The Voyager package also implements plotting functions to plot SFE data and ESDA results.

Maintained by Lambda Moses. Last updated 3 months ago.

geneexpression spatial transcriptomics visualization bioconductor eda esda exploratory-data-analysis omics spatial-statistics spatial-transcriptomics

45.6 match 88 stars 8.71 score 173 scripts

usdaforestservice

FIESTA:Forest Inventory Estimation and Analysis

A research estimation tool for analysts that work with sample-based inventory data from the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program.

Maintained by Grayson White. Last updated 2 days ago.

41.0 match 30 stars 8.84 score 62 scripts

jeffreyevans

spatialEco:Spatial Analysis and Modelling Utilities

Utilities to support spatial data manipulation, query, sampling and modelling in ecological applications. Functions include models for species population density, spatial smoothing, multivariate separability, point process model for creating pseudo- absences and sub-sampling, Quadrant-based sampling and analysis, auto-logistic modeling, sampling models, cluster optimization, statistical exploratory tools and raster-based metrics.

Maintained by Jeffrey S. Evans. Last updated 25 days ago.

biodiversity conservation ecology r-spatial raster spatial vector

36.4 match 110 stars 9.55 score 736 scripts 2 dependents

hannameyer

CAST:'caret' Applications for Spatial-Temporal Models

Supporting functionality to run 'caret' with spatial or spatial-temporal data. 'caret' is a frequently used package for model training and prediction using machine learning. CAST includes functions to improve spatial or spatial-temporal modelling tasks using 'caret'. It includes the newly suggested 'Nearest neighbor distance matching' cross-validation to estimate the performance of spatial prediction models and allows for spatial variable selection to selects suitable predictor variables in view to their contribution to the spatial model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models. Methods are described in Meyer et al. (2018) <doi:10.1016/j.envsoft.2017.12.001>; Meyer et al. (2019) <doi:10.1016/j.ecolmodel.2019.108815>; Meyer and Pebesma (2021) <doi:10.1111/2041-210X.13650>; Milà et al. (2022) <doi:10.1111/2041-210X.13851>; Meyer and Pebesma (2022) <doi:10.1038/s41467-022-29838-9>; Linnenbrink et al. (2023) <doi:10.5194/egusphere-2023-1308>; Schumacher et al. (2024) <doi:10.5194/egusphere-2024-2730>. The package is described in detail in Meyer et al. (2024) <doi:10.48550/arXiv.2404.06978>.

Maintained by Hanna Meyer. Last updated 2 months ago.

autocorrelation caret feature-selection machine-learning overfitting predictive-modeling spatial spatio-temporal variable-selection

28.9 match 114 stars 11.97 score 298 scripts 1 dependents

adeverse

adespatial:Multivariate Multiscale Spatial Analysis

Tools for the multiscale spatial analysis of multivariate data. Several methods are based on the use of a spatial weighting matrix and its eigenvector decomposition (Moran's Eigenvectors Maps, MEM). Several approaches are described in the review Dray et al (2012) <doi:10.1890/11-1183.1>.

Maintained by Aurélie Siberchicot. Last updated 9 days ago.

fortran openblas

30.7 match 36 stars 11.16 score 398 scripts 2 dependents

ausgis

geocomplexity:Mitigating Spatial Bias Through Geographical Complexity

The geographical complexity of individual variables can be characterized by the differences in local attribute variables, while the common geographical complexity of multiple variables can be represented by fluctuations in the similarity of vectors composed of multiple variables. In spatial regression tasks, the goodness of fit can be improved by incorporating a geographical complexity representation vector during modeling, using a geographical complexity-weighted spatial weight matrix, or employing local geographical complexity kernel density. Similarly, in spatial sampling tasks, samples can be selected more effectively by using a method that weights based on geographical complexity. By optimizing performance in spatial regression and spatial sampling tasks, the spatial bias of the model can be effectively reduced.

Maintained by Wenbo Lv. Last updated 5 months ago.

geographical-complexity geospatial-analysis spatial-regression spatial-relations spatial-sampling spatial-statistics openblas cpp openmp

51.9 match 19 stars 6.53 score 12 scripts

dnychka

fields:Tools for Spatial Data

For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding the source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development of this package was supported in part by the National Science Foundation Grant 1417857, the National Center for Atmospheric Research, and Colorado School of Mines. See the Fields URL for a vignette on using this package and some background on spatial statistics.

Maintained by Douglas Nychka. Last updated 9 months ago.

fortran

23.8 match 15 stars 12.70 score 7.7k scripts 297 dependents

bodkan

slendr:A Simulation Framework for Spatiotemporal Population Genetics

A framework for simulating spatially explicit genomic data which leverages real cartographic information for programmatic and visual encoding of spatiotemporal population dynamics on real geographic landscapes. Population genetic models are then automatically executed by the 'SLiM' software by Haller et al. (2019) <doi:10.1093/molbev/msy228> behind the scenes, using a custom built-in simulation 'SLiM' script. Additionally, fully abstract spatial models not tied to a specific geographic location are supported, and users can also simulate data from standard, non-spatial, random-mating models. These can be simulated either with the 'SLiM' built-in back-end script, or using an efficient coalescent population genetics simulator 'msprime' by Baumdicker et al. (2022) <doi:10.1093/genetics/iyab229> with a custom-built 'Python' script bundled with the R package. Simulated genomic data is saved in a tree-sequence format and can be loaded, manipulated, and summarised using tree-sequence functionality via an R interface to the 'Python' module 'tskit' by Kelleher et al. (2019) <doi:10.1038/s41588-019-0483-y>. Complete model configuration, simulation and analysis pipelines can be therefore constructed without a need to leave the R environment, eliminating friction between disparate tools for population genetic simulations and data analysis.

Maintained by Martin Petr. Last updated 10 hours ago.

popgen population-genetics simulations spatial-statistics

32.4 match 56 stars 9.13 score 88 scripts

stscl

sdsfun:Spatial Data Science Complementary Features

Wrapping and supplementing commonly used functions in the R ecosystem related to spatial data science, while serving as a basis for other packages maintained by Wenbo Lv.

Maintained by Wenbo Lv. Last updated 27 days ago.

geoinformatics spatial-data-analysis spatial-data-science spatial-statistics openblas cpp openmp

44.7 match 16 stars 6.58 score 6 scripts 8 dependents

rominsal

spsur:Spatial Seemingly Unrelated Regression Models

A collection of functions to test and estimate Seemingly Unrelated Regression (usually called SUR) models, with spatial structure, by maximum likelihood and three-stage least squares. The package estimates the most common spatial specifications, that is, SUR with Spatial Lag of X regressors (called SUR-SLX), SUR with Spatial Lag Model (called SUR-SLM), SUR with Spatial Error Model (called SUR-SEM), SUR with Spatial Durbin Model (called SUR-SDM), SUR with Spatial Durbin Error Model (called SUR-SDEM), SUR with Spatial Autoregressive terms and Spatial Autoregressive Disturbances (called SUR-SARAR), SUR-SARAR with Spatial Lag of X regressors (called SUR-GNM) and SUR with Spatially Independent Model (called SUR-SIM). The methodology of these models can be found in next references: Mur, J., Lopez, F., and Herrera, M. (2010) <doi:10.1080/17421772.2010.516443>; Lopez, F.A., Mur, J., and Angulo, A. (2014) <doi:10.1007/s00168-014-0624-2> and Lopez, F.A., Minguez, R. and Mur, J. (2020) <doi:10.1007/s00168-019-00914-1>.

Maintained by Roman Minguez. Last updated 3 years ago.

47.2 match 11 stars 6.15 score 32 scripts

ausgis

GD:Geographical Detectors for Assessing Spatial Factors

Geographical detectors for measuring spatial stratified heterogeneity, as described in Jinfeng Wang (2010) <doi:10.1080/13658810802443457> and Jinfeng Wang (2016) <doi:10.1016/j.ecolind.2016.02.052>. Includes the optimal discretization of continuous data, four primary functions of geographical detectors, comparison of size effects of spatial unit and the visualizations of results. To use the package and to refer the descriptions of the package, methods and case datasets, please cite Yongze Song (2020) <doi:10.1080/15481603.2020.1760434>. The model has been applied in factor exploration of road performance and multi-scale spatial segmentation for network data, as described in Yongze Song (2018) <doi:10.3390/rs10111696> and Yongze Song (2020) <doi:10.1109/TITS.2020.3001193>, respectively.

Maintained by Wenbo Lv. Last updated 4 months ago.

geographical-detector spatial-stratified-heterogeneity

37.3 match 13 stars 7.74 score 51 scripts

jeremygelb

spNetwork:Spatial Analysis on Network

Perform spatial analysis on network. Implement several methods for spatial analysis on network: Network Kernel Density estimation, building of spatial matrices based on network distance ('listw' objects from 'spdep' package), K functions estimation for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation References: Okabe et al (2019) <doi:10.1080/13658810802475491>; Okabe et al (2012, ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200).

Maintained by Jeremy Gelb. Last updated 14 days ago.

kernel kernel-density-estimation network network-analysis spatial spatial-analysis spatial-data-analysis cpp

37.3 match 38 stars 7.69 score 52 scripts

jpearson0525

micromapST:Linked Micromap Plots for U. S. and Other Geographic Areas

Provides the users with the ability to quickly create linked micromap plots for a collection of geographic areas. Linked micromap plots are visualizations of geo-referenced data that link statistical graphics to an organized series of small maps or graphic images. The Help description contains examples of how to use the 'micromapST' function. Contained in this package are border group datasets to support creating linked micromap plots for the 50 U.S. states and District of Columbia (51 areas), the U. S. 20 Seer Registries, the 105 counties in the state of Kansas, the 62 counties of New York, the 24 counties of Maryland, the 29 counties of Utah, the 32 administrative areas in China, the 218 administrative areas in the UK and Ireland (for testing only), the 25 districts in the city of Seoul South Korea, and the 52 counties on the Africa continent. A border group dataset contains the boundaries related to the data level areas, a second layer boundaries, a top or third layer boundary, a parameter list of run options, and a cross indexing table between area names, abbreviations, numeric identification and alias matching strings for the specific geographic area. By specifying a border group, the package create linked micromap plots for any geographic region. The user can create and provide their own border group dataset for any area beyond the areas contained within the package. In version 3.0.0, the 'BuildBorderGroup' function was upgraded to not use the retiring 'maptools', 'rgdal', and 'rgeos' packages. References: Carr and Pickle, Chapman and Hall/CRC, Visualizing Data Patterns with Micromaps, CRC Press, 2010. Pickle, Pearson, and Carr (2015), micromapST: Exploring and Communicating Geospatial Patterns in US State Data., Journal of Statistical Software, 63(3), 1-25., <https://www.jstatsoft.org/v63/i03/>. Copyrighted 2013, 2014, 2015, 2016, 2022, 2023, 2024, and 2025 by Carr, Pearson and Pickle.

Maintained by Jim Pearson. Last updated 1 months ago.

101.7 match 2.80 score 21 scripts

spatstat

spatstat.data:Datasets for 'spatstat' Family

Contains all the datasets for the 'spatstat' family of packages.

Maintained by Adrian Baddeley. Last updated 12 days ago.

kernel-density point-process spatial-analysis spatial-data spatial-data-analysis spatstat statistical-analysis statistical-methods statistical-tests statistics

25.3 match 6 stars 11.07 score 186 scripts 228 dependents

satijalab

Seurat:Tools for Single Cell Genomics

A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.

Maintained by Paul Hoffman. Last updated 1 years ago.

human-cell-atlas single-cell-genomics single-cell-rna-seq cpp

16.6 match 2.4k stars 16.86 score 50k scripts 73 dependents

bioc

Banksy:Spatial transcriptomic clustering

Banksy is an R package that incorporates spatial information to cluster cells in a feature space (e.g. gene expression). To incorporate spatial information, BANKSY computes the mean neighborhood expression and azimuthal Gabor filters that capture gene expression gradients. These features are combined with the cell's own expression to embed cells in a neighbor-augmented product space which can then be clustered, allowing for accurate and spatially-aware cell typing and tissue domain segmentation.

Maintained by Joseph Lee. Last updated 25 days ago.

clustering spatial singlecell geneexpression dimensionreduction clustering-algorithm single-cell-omics spatial-omics

30.9 match 90 stars 9.03 score 248 scripts

rominsal

pspatreg:Spatial and Spatio-Temporal Semiparametric Regression Models with Spatial Lags

Estimation and inference of spatial and spatio-temporal semiparametric models including spatial or spatio-temporal non-parametric trends, parametric and non-parametric covariates and, possibly, a spatial lag for the dependent variable and temporal correlation in the noise. The spatio-temporal trend can be decomposed in ANOVA way including main and interaction functional terms. Use of SAP algorithm to estimate the spatial or spatio-temporal trend and non-parametric covariates. The methodology of these models can be found in next references Basile, R. et al. (2014), <doi:10.1016/j.jedc.2014.06.011>; Rodriguez-Alvarez, M.X. et al. (2015) <doi:10.1007/s11222-014-9464-2> and, particularly referred to the focus of the package, Minguez, R., Basile, R. and Durban, M. (2020) <doi:10.1007/s10260-019-00492-8>.

Maintained by Roman Minguez. Last updated 3 years ago.

42.2 match 12 stars 6.44 score 77 scripts

blasbenito

spatialRF:Easy Spatial Modeling with Random Forest

Automatic generation and selection of spatial predictors for spatial regression with Random Forest. Spatial predictors are surrogates of variables driving the spatial structure of a response variable. The package offers two methods to generate spatial predictors from a distance matrix among training cases: 1) Moran's Eigenvector Maps (MEMs; Dray, Legendre, and Peres-Neto 2006 <DOI:10.1016/j.ecolmodel.2006.02.015>): computed as the eigenvectors of a weighted matrix of distances; 2) RFsp (Hengl et al. <DOI:10.7717/peerj.5518>): columns of the distance matrix used as spatial predictors. Spatial predictors help minimize the spatial autocorrelation of the model residuals and facilitate an honest assessment of the importance scores of the non-spatial predictors. Additionally, functions to reduce multicollinearity, identify relevant variable interactions, tune random forest hyperparameters, assess model transferability via spatial cross-validation, and explore model results via partial dependence curves and interaction surfaces are included in the package. The modelling functions are built around the highly efficient 'ranger' package (Wright and Ziegler 2017 <DOI:10.18637/jss.v077.i01>).

Maintained by Blas M. Benito. Last updated 3 years ago.

random-forest spatial-analysis spatial-regression

49.1 match 114 stars 5.45 score 49 scripts

ksawicka

spup:Spatial Uncertainty Propagation Analysis

Uncertainty propagation analysis in spatial environmental modelling following methodology described in Heuvelink et al. (2007) <doi:10.1080/13658810601063951> and Brown and Heuvelink (2007) <doi:10.1016/j.cageo.2006.06.015>. The package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model outputs. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques. Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is accommodated for. The MC realizations may be used as input to the environmental models called from R, or externally.

Maintained by Kasia Sawicka. Last updated 1 years ago.

monte-carlo spatial uncertainty-analysis uncertainty-propagation

42.3 match 9 stars 6.31 score 57 scripts

paleolimbot

ggspatial:Spatial Data Framework for ggplot2

Spatial data plus the power of the ggplot2 framework means easier mapping when input data are already in the form of spatial objects.

Maintained by Dewey Dunnington. Last updated 2 years ago.

20.5 match 379 stars 12.85 score 4.1k scripts 24 dependents

rafapereirabr

geobr:Download Official Spatial Data Sets of Brazil

Easy access to official spatial data sets of Brazil as 'sf' objects in R. The package includes a wide range of geospatial data available at various geographic scales and for various years with harmonized attributes, projection and fixed topology.

Maintained by Rafael H. M. Pereira. Last updated 7 months ago.

44.7 match 5.89 score 1.4k scripts 1 dependents

r-lidar

lidR:Airborne LiDAR Data Manipulation and Visualization for Forestry Applications

Airborne LiDAR (Light Detection and Ranging) interface for data manipulation and visualization. Read/write 'las' and 'laz' files, computation of metrics in area based approach, point filtering, artificial point reduction, classification from geographic data, normalization, individual tree segmentation and other manipulations.

Maintained by Jean-Romain Roussel. Last updated 2 months ago.

als forestry las laz lidar point-cloud remote-sensing openblas cpp openmp

17.9 match 623 stars 14.47 score 844 scripts 8 dependents

usepa

spmodel:Spatial Statistical Modeling and Prediction

Fit, summarize, and predict for a variety of spatial statistical models applied to point-referenced and areal (lattice) data. Parameters are estimated using various methods. Additional modeling features include anisotropy, non-spatial random effects, partition factors, big data approaches, and more. Model-fit statistics are used to summarize, visualize, and compare models. Predictions at unobserved locations are readily obtainable. For additional details, see Dumelle et al. (2023) <doi:10.1371/journal.pone.0282524>.

Maintained by Michael Dumelle. Last updated 16 days ago.

33.3 match 15 stars 7.66 score 112 scripts 3 dependents

t-kalinowski

keras:R Interface to 'Keras'

Interface to 'Keras' <https://keras.io>, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.

Maintained by Tomasz Kalinowski. Last updated 11 months ago.

22.8 match 10.93 score 10k scripts 55 dependents

bioc

SpatialFeatureExperiment:Integrating SpatialExperiment with Simple Features in sf

A new S4 class integrating Simple Features with the R package sf to bring geospatial data analysis methods based on vector data to spatial transcriptomics. Also implements management of spatial neighborhood graphs and geometric operations. This pakage builds upon SpatialExperiment and SingleCellExperiment, hence methods for these parent classes can still be used.

Maintained by Lambda Moses. Last updated 2 months ago.

datarepresentation transcriptomics spatial

25.9 match 49 stars 9.40 score 322 scripts 1 dependents

r-spatial

classInt:Choose Univariate Class Intervals

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

Maintained by Roger Bivand. Last updated 3 months ago.

fortran

15.0 match 34 stars 16.17 score 3.2k scripts 1.2k dependents

mlr-org

mlr3spatiotempcv:Spatiotemporal Resampling Methods for 'mlr3'

Extends the mlr3 machine learning framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation (STAC) in predictor variables. STAC may cause highly biased performance estimates in cross-validation if ignored. A JSS article is available at <doi:10.18637/jss.v111.i07>.

Maintained by Patrick Schratz. Last updated 4 months ago.

cross-validation mlr3 resampling resampling-methods spatial temporal

30.0 match 50 stars 8.09 score 123 scripts

bioc

spatialHeatmap:spatialHeatmap: Visualizing Spatial Assays in Anatomical Images and Large-Scale Data Extensions

The spatialHeatmap package offers the primary functionality for visualizing cell-, tissue- and organ-specific assay data in spatial anatomical images. Additionally, it provides extended functionalities for large-scale data mining routines and co-visualizing bulk and single-cell data. A description of the project is available here: https://spatialheatmap.org.

Maintained by Jianhai Zhang. Last updated 4 months ago.

spatial visualization microarray sequencing geneexpression datarepresentation network clustering graphandnetwork cellbasedassays atacseq dnaseq tissuemicroarray singlecell cellbiology genetarget

38.7 match 5 stars 6.26 score 12 scripts

damianobaldan

RAC:R Package for Aqua Culture

Solves the individual bioenergetic balance for different aquaculture sea fish (Sea Bream and Sea Bass; Brigolin et al., 2014 <doi:10.3354/aei00093>) and shellfish (Mussel and Clam; Brigolin et al., 2009 <doi:10.1016/j.ecss.2009.01.029>; Solidoro et al., 2000 <doi:10.3354/meps199137>). Allows for spatialized model runs and population simulations.

Maintained by Baldan D.. Last updated 2 years ago.

52.7 match 4.54 score

spatlyu

tidyrgeoda:A tidy interface for rgeoda

An interface for 'rgeoda' to integrate with 'sf' objects and the 'tidyverse'.

Maintained by Wenbo Lv. Last updated 7 months ago.

geocomputation geoinformatics giscience spatial-analysis spatial-statistics

46.6 match 16 stars 5.11 score 5 scripts

bioc

alabaster.spatial:Save and Load Spatial 'Omics Data to/from File

Save SpatialExperiment objects and their images into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

Maintained by Aaron Lun. Last updated 5 months ago.

dataimport datarepresentation

47.3 match 5.02 score 5 scripts 1 dependents

bioc

CARDspa:Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics

CARD is a reference-based deconvolution method that estimates cell type composition in spatial transcriptomics based on cell type specific expression information obtained from a reference scRNA-seq data. A key feature of CARD is its ability to accommodate spatial correlation in the cell type composition across tissue locations, enabling accurate and spatially informed cell type deconvolution as well as refined spatial map construction. CARD relies on an efficient optimization algorithm for constrained maximum likelihood estimation and is scalable to spatial transcriptomics with tens of thousands of spatial locations and tens of thousands of genes.

Maintained by Jing Fu. Last updated 1 days ago.

spatial singlecell transcriptomics visualization openblas cpp openmp

51.4 match 4.60 score 3 scripts

bioc

SpatialExperiment:S4 Class for Spatially Resolved -omics Data

Defines an S4 class for storing data from spatial -omics experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based platforms, including spatial coordinates, images, and image metadata. A specialized constructor function is included for data from the 10x Genomics Visium platform.

Maintained by Dario Righelli. Last updated 5 months ago.

datarepresentation dataimport infrastructure immunooncology geneexpression transcriptomics singlecell spatial

18.7 match 59 stars 12.63 score 1.8k scripts 71 dependents

mobiodiv

mobsim:Spatial Simulation and Scale-Dependent Analysis of Biodiversity Changes

Simulation, analysis and sampling of spatial biodiversity data (May, Gerstner, McGlinn, Xiao & Chase 2017) <doi:10.1111/2041-210x.12986>. In the simulation tools user define the numbers of species and individuals, the species abundance distribution and species aggregation. Functions for analysis include species rarefaction and accumulation curves, species-area relationships and the distance decay of similarity.

Maintained by Felix May. Last updated 4 months ago.

biodiversity macroecology point-pattern-analysis rarefaction simulation species species-abundance-distributions cpp

28.8 match 20 stars 7.84 score 76 scripts

paulojus

geoR:Analysis of Geostatistical Data

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>.

Maintained by Paulo Justiniano Ribeiro Jr. Last updated 1 years ago.

29.6 match 10 stars 7.57 score 1.8k scripts 12 dependents

arinams

saeHB.spatial:Small Area Estimation Hierarchical Bayes For Spatial Model

Provides several functions and datasets for area level of Small Area Estimation under Spatial Model using Hierarchical Bayesian (HB) Method. Model-based estimators include the HB estimators based on a Spatial Fay-Herriot model with univariate normal distribution for variable of interest.The 'rjags' package is employed to obtain parameter estimates. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.

Maintained by Arina Mana Sikana. Last updated 4 months ago.

jags cpp

55.2 match 4.00 score 6 scripts

josiahparry

sfdep:Spatial Dependence for Simple Features

An interface to 'spdep' to integrate with 'sf' objects and the 'tidyverse'.

Maintained by Dexter Locke. Last updated 7 months ago.

r-spatial spatial

31.4 match 130 stars 7.01 score 130 scripts

bioc

CatsCradle:This package provides methods for analysing spatial transcriptomics data and for discovering gene clusters

This package addresses two broad areas. It allows for in-depth analysis of spatial transcriptomic data by identifying tissue neighbourhoods. These are contiguous regions of tissue surrounding individual cells. 'CatsCradle' allows for the categorisation of neighbourhoods by the cell types contained in them and the genes expressed in them. In particular, it produces Seurat objects whose individual elements are neighbourhoods rather than cells. In addition, it enables the categorisation and annotation of genes by producing Seurat objects whose elements are genes.

Maintained by Michael Shapiro. Last updated 12 days ago.

biologicalquestion statisticalmethod geneexpression singlecell transcriptomics spatial

33.7 match 3 stars 6.52 score

r-spatial

s2:Spherical Geometry Operators Using the S2 Geometry Library

Provides R bindings for Google's s2 library for geometric calculations on the sphere. High-performance constructors and exporters provide high compatibility with existing spatial packages, transformers construct new geometries from existing geometries, predicates provide a means to select geometries based on spatial relationships, and accessors extract information about geometries.

Maintained by Edzer Pebesma. Last updated 12 days ago.

openssl cpp

15.8 match 74 stars 13.76 score 207 scripts 1.2k dependents

giscience-fsu

sperrorest:Perform Spatial Error Estimation and Variable Importance Assessment

Implements spatial error estimation and permutation-based variable importance measures for predictive models using spatial cross-validation and spatial block bootstrap.

Maintained by Alexander Brenning. Last updated 2 years ago.

cross-validation machine-learning spatial-statistics spatio-temporal-modeling statistical-learning

33.4 match 19 stars 6.46 score 46 scripts

rstudio

keras3:R Interface to 'Keras'

Interface to 'Keras' <https://keras.io>, a high-level neural networks API. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.

Maintained by Tomasz Kalinowski. Last updated 8 days ago.

15.9 match 845 stars 13.63 score 264 scripts 2 dependents

objornstad

ncf:Spatial Covariance Functions

Spatial (cross-)covariance and related geostatistical tools: the nonparametric (cross-)covariance function , the spline correlogram, the nonparametric phase coherence function, local indicators of spatial association (LISA), (Mantel) correlogram, (Partial) Mantel test.

Maintained by Ottar N. Bjornstad. Last updated 3 years ago.

33.5 match 5 stars 6.44 score 328 scripts 1 dependents

bioc

mistyR:Multiview Intercellular SpaTial modeling framework

mistyR is an implementation of the Multiview Intercellular SpaTialmodeling framework (MISTy). MISTy is an explainable machine learning framework for knowledge extraction and analysis of single-cell, highly multiplexed, spatially resolved data. MISTy facilitates an in-depth understanding of marker interactions by profiling the intra- and intercellular relationships. MISTy is a flexible framework able to process a custom number of views. Each of these views can describe a different spatial context, i.e., define a relationship among the observed expressions of the markers, such as intracellular regulation or paracrine regulation, but also, the views can also capture cell-type specific relationships, capture relations between functional footprints or focus on relations between different anatomical regions. Each MISTy view is considered as a potential source of variability in the measured marker expressions. Each MISTy view is then analyzed for its contribution to the total expression of each marker and is explained in terms of the interactions with other measurements that led to the observed contribution.

Maintained by Jovan Tanevski. Last updated 5 months ago.

software biomedicalinformatics cellbiology systemsbiology regression decisiontree singlecell spatial bioconductor biology intercellular machine-learning modular molecular-biology multiview spatial-transcriptomics

27.3 match 51 stars 7.87 score 160 scripts

spatstat

spatstat.random:Random Generation Functionality for the 'spatstat' Family

Functionality for random generation of spatial data in the 'spatstat' family of packages. Generates random spatial patterns of points according to many simple rules (complete spatial randomness, Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns (thinning, random shift, jittering), simulated realisations of random point processes including simple sequential inhibition, Matern inhibition models, Neyman-Scott cluster processes (using direct, Brix-Kendall, or hybrid algorithms), log-Gaussian Cox processes, product shot noise cluster processes and Gibbs point processes (using Metropolis-Hastings birth-death-shift algorithm, alternating Gibbs sampler, or coupling-from-the-past perfect simulation). Also generates random spatial patterns of line segments, random tessellations, and random images (random noise, random mosaics). Excludes random generation on a linear network, which is covered by the separate package 'spatstat.linnet'.

Maintained by Adrian Baddeley. Last updated 10 days ago.

point-processes random-generation simulation spatial-sampling spatial-simulation cpp

19.7 match 5 stars 10.85 score 84 scripts 175 dependents

tbep-tech

tbeptools:Data and Indicators for the Tampa Bay Estuary Program

Several functions are provided for working with Tampa Bay Estuary Program data and indicators, including the water quality report card, tidal creek assessments, Tampa Bay Nekton Index, Tampa Bay Benthic Index, seagrass transect data, habitat report card, and fecal indicator bacteria. Additional functions are provided for miscellaneous tasks, such as reference library curation.

Maintained by Marcus Beck. Last updated 2 days ago.

data-analysis tampa-bay tbep water-quality

27.2 match 10 stars 7.86 score 133 scripts

bioc

BulkSignalR:Infer Ligand-Receptor Interactions from bulk expression (transcriptomics/proteomics) data, or spatial transcriptomics

Inference of ligand-receptor (LR) interactions from bulk expression (transcriptomics/proteomics) data, or spatial transcriptomics. BulkSignalR bases its inferences on the LRdb database included in our other package, SingleCellSignalR available from Bioconductor. It relies on a statistical model that is specific to bulk data sets. Different visualization and data summary functions are proposed to help navigating prediction results.

Maintained by Jean-Philippe Villemin. Last updated 3 months ago.

network rnaseq software proteomics transcriptomics networkinference spatial

40.6 match 5.22 score 15 scripts

bioc

escheR:Unified multi-dimensional visualizations with Gestalt principles

The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to spatially-resolved transcriptomics data, but also broadly to data visualized in 2D space, such as embedding visualizations. We provide this open source R package escheR, which is built off of the state-of-the-art ggplot2 visualization framework and can be seamlessly integrated into genomics toolboxes and workflows.

Maintained by Boyi Guo. Last updated 5 months ago.

spatial singlecell transcriptomics visualization software multidimensional single-cell spatial-omics

31.2 match 6 stars 6.74 score 153 scripts 1 dependents

riatelab

mapsf:Thematic Cartography

Create and integrate thematic maps in your workflow. This package helps to design various cartographic representations such as proportional symbols, choropleth or typology maps. It also offers several functions to display layout elements that improve the graphic presentation of maps (e.g. scale bar, north arrow, title, labels). 'mapsf' maps 'sf' objects on 'base' graphics.

Maintained by Timothée Giraud. Last updated 11 days ago.

cartography map spatial spatial-analysis

18.5 match 229 stars 11.32 score 414 scripts 12 dependents

bioc

SpotClean:SpotClean adjusts for spot swapping in spatial transcriptomics data

SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontaminate the spot swapping effect, thus increase the sensitivity and precision of downstream analyses.

Maintained by Zijian Ni. Last updated 5 months ago.

dataimport rnaseq sequencing geneexpression spatial singlecell transcriptomics preprocessing rna-seq spatial-transcriptomics

31.7 match 31 stars 6.52 score 36 scripts

r-spatial

leafem:'leaflet' Extensions for 'mapview'

Provides extensions for packages 'leaflet' & 'mapdeck', many of which are used by package 'mapview'. Focus is on functionality readily available in Geographic Information Systems such as 'Quantum GIS'. Includes functions to display coordinates of mouse pointer position, query image values via mouse pointer and zoom-to-layer buttons. Additionally, provides a feature type agnostic function to add points, lines, polygons to a map.

Maintained by Tim Appelhans. Last updated 30 days ago.

16.7 match 108 stars 12.36 score 704 scripts 53 dependents

mazamascience

MazamaSpatialUtils:Spatial Data Download and Utility Functions

A suite of conversion functions to create internally standardized spatial polygons data frames. Utility functions use these data sets to return values such as country, state, time zone, watershed, etc. associated with a set of longitude/latitude pairs. (They also make cool maps.)

Maintained by Jonathan Callahan. Last updated 5 months ago.

25.4 match 5 stars 8.09 score 282 scripts 2 dependents

hemingnm

SESraster:Raster Randomization for Null Hypothesis Testing

Randomization of presence/absence species distribution raster data with or without including spatial structure for calculating standardized effect sizes and testing null hypothesis. The randomization algorithms are based on classical algorithms for matrices (Gotelli 2000, <doi:10.2307/177478>) implemented for raster data.

Maintained by Neander Marcel Heming. Last updated 5 months ago.

null-models randomization raster spatial spatial-analysis species-distribution-modelling

31.0 match 7 stars 6.61 score 32 scripts 2 dependents

mstrimas

smoothr:Smooth and Tidy Spatial Features

Tools for smoothing and tidying spatial features (i.e. lines and polygons) to make them more aesthetically pleasing. Smooth curves, fill holes, and remove small fragments from lines and polygons.

Maintained by Matthew Strimas-Mackey. Last updated 2 years ago.

gis spatial

21.4 match 100 stars 9.53 score 440 scripts 9 dependents

adamlilith

fasterRaster:Faster Raster and Spatial Vector Processing Using 'GRASS GIS'

Processing of large-in-memory/large-on disk rasters and spatial vectors using 'GRASS GIS' <https://grass.osgeo.org/>. Most functions in the 'terra' package are recreated. Processing of medium-sized and smaller spatial objects will nearly always be faster using 'terra' or 'sf', but for large-in-memory/large-on-disk objects, 'fasterRaster' may be faster. To use most of the functions, you must have the stand-alone version (not the 'OSGeoW4' installer version) of 'GRASS GIS' 8.0 or higher.

Maintained by Adam B. Smith. Last updated 2 days ago.

aspect distance fragmentation fragmentation-indices gis grass grass-gis raster raster-projection rasterize slope topography vectorization

26.6 match 57 stars 7.68 score 8 scripts

bioc

jazzPanda:Finding spatially relevant marker genes in image based spatial transcriptomics data

This package contains the function to find marker genes for image-based spatial transcriptomics data. There are functions to create spatial vectors from the cell and transcript coordiantes, which are passed as inputs to find marker genes. Marker genes are detected for every cluster by two approaches. The first approach is by permtuation testing, which is implmented in parallel for finding marker genes for one sample study. The other approach is to build a linear model for every gene. This approach can account for multiple samples and backgound noise.

Maintained by Melody Jin. Last updated 27 days ago.

spatial geneexpression differentialexpression statisticalmethod transcriptomics correlation linear-models marker-genes spatial-transcriptomics

40.5 match 2 stars 5.00 score

feiyoung

DR.SC:Joint Dimension Reduction and Spatial Clustering

Joint dimension reduction and spatial clustering is conducted for Single-cell RNA sequencing and spatial transcriptomics data, and more details can be referred to Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou, Xingjie Shi and Jin Liu. (2022) <doi:10.1093/nar/gkac219>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.

Maintained by Wei Liu. Last updated 1 years ago.

dimension-reduction selfsupervised spatial-clustering spatial-transcriptomics openblas cpp

32.9 match 5 stars 6.12 score 29 scripts 2 dependents

egpivo

SpatPCA:Regularized Principal Component Analysis for Spatial Data

Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, <DOI:10.1080/10618600.2016.1157483>). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D.

Maintained by Wen-Ting Wang. Last updated 7 months ago.

admm covariance-estimation eigenfunctions lasso matrix-factorization pca rcpparmadillo rcppparallel regularization spatial spatial-data-analysis splines openblas cpp openmp

36.1 match 20 stars 5.53 score 17 scripts

stscl

sesp:Spatially Explicit Stratified Power

Assesses spatial associations between variables through an equivalent geographical detector (q-statistic) within a regression framework and incorporates a spatially explicit stratified power model by integrating spatial dependence and spatial stratified heterogeneity, facilitating the modeling of complex spatial relationships.

Maintained by Wenbo Lv. Last updated 3 months ago.

spatial-explicit-geographical-detector spatial-stratified-heterogeneity cpp

36.6 match 15 stars 5.43 score

nowosad

sabre:Spatial Association Between Regionalizations

Calculates a degree of spatial association between regionalizations or categorical maps using the information-theoretical V-measure (Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>). It also offers an R implementation of the MapCurve method (Hargrove et al. (2006) <doi:10.1007/s10109-006-0025-x>).

Maintained by Jakub Nowosad. Last updated 3 months ago.

entropy polygons regionalizations spatial spatial-analysis

28.4 match 36 stars 6.95 score 25 scripts

bioc

Cardinal:A mass spectrometry imaging toolbox for statistical analysis

Implements statistical & computational tools for analyzing mass spectrometry imaging datasets, including methods for efficient pre-processing, spatial segmentation, and classification.

Maintained by Kylie Ariel Bemis. Last updated 3 months ago.

software infrastructure proteomics lipidomics massspectrometry imagingmassspectrometry immunooncology normalization clustering classification regression

19.1 match 48 stars 10.32 score 200 scripts

jinli22

spm:Spatial Predictive Modeling

Introduction to some novel accurate hybrid methods of geostatistical and machine learning methods for spatial predictive modelling. It contains two commonly used geostatistical methods, two machine learning methods, four hybrid methods and two averaging methods. For each method, two functions are provided. One function is for assessing the predictive errors and accuracy of the method based on cross-validation. The other one is for generating spatial predictions using the method. For details please see: Li, J., Potter, A., Huang, Z., Daniell, J. J. and Heap, A. (2010) <https:www.ga.gov.au/metadata-gateway/metadata/record/gcat_71407> Li, J., Heap, A. D., Potter, A., Huang, Z. and Daniell, J. (2011) <doi:10.1016/j.csr.2011.05.015> Li, J., Heap, A. D., Potter, A. and Daniell, J. (2011) <doi:10.1016/j.envsoft.2011.07.004> Li, J., Potter, A., Huang, Z. and Heap, A. (2012) <https:www.ga.gov.au/metadata-gateway/metadata/record/74030>.

Maintained by Jin Li. Last updated 3 years ago.

35.9 match 3 stars 5.46 score 107 scripts 3 dependents

r-spatial

RSAGA:SAGA Geoprocessing and Terrain Analysis

Provides access to geocomputing and terrain analysis functions of the geographical information system (GIS) 'SAGA' (System for Automated Geoscientific Analyses) from within R by running the command line version of SAGA. This package furthermore provides several R functions for handling ASCII grids, including a flexible framework for applying local functions (including predict methods of fitted models) and focal functions to multiple grids. SAGA GIS is available under GPL-2 / LGPL-2 licences from <https://sourceforge.net/projects/saga-gis/>.

Maintained by Alexander Brenning. Last updated 2 months ago.

22.5 match 23 stars 8.72 score 275 scripts

r-spatial

lwgeom:Bindings to Selected 'liblwgeom' Functions for Simple Features

Access to selected functions found in 'liblwgeom' <https://github.com/postgis/postgis/tree/master/liblwgeom>, the light-weight geometry library used by 'PostGIS' <http://postgis.net/>.

Maintained by Edzer Pebesma. Last updated 2 months ago.

proj geos cpp

15.0 match 61 stars 12.95 score 1.7k scripts 66 dependents

pbs-assess

sdmTMB:Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB'

Implements spatial and spatiotemporal GLMMs (Generalized Linear Mixed Effect Models) using 'TMB', 'fmesher', and the SPDE (Stochastic Partial Differential Equation) Gaussian Markov random field approximation to Gaussian random fields. One common application is for spatially explicit species distribution models (SDMs). See Anderson et al. (2024) <doi:10.1101/2022.03.24.485545>.

Maintained by Sean C. Anderson. Last updated 2 days ago.

ecology glmm spatial-analysis species-distribution-modelling tmb cpp

17.5 match 205 stars 11.04 score 848 scripts 1 dependents

jfrench

smerc:Statistical Methods for Regional Counts

Implements statistical methods for analyzing the counts of areal data, with a focus on the detection of spatial clusters and clustering. The package has a heavy emphasis on spatial scan methods, which were first introduced by Kulldorff and Nagarwalla (1995) <doi:10.1002/sim.4780140809> and Kulldorff (1997) <doi:10.1080/03610929708831995>.

Maintained by Joshua French. Last updated 5 months ago.

cpp

31.4 match 3 stars 6.08 score 45 scripts 3 dependents

ropensci

waywiser:Ergonomic Methods for Assessing Spatial Models

Assessing predictive models of spatial data can be challenging, both because these models are typically built for extrapolating outside the original region represented by training data and due to potential spatially structured errors, with "hot spots" of higher than expected error clustered geographically due to spatial structure in the underlying data. Methods are provided for assessing models fit to spatial data, including approaches for measuring the spatial structure of model errors, assessing model predictions at multiple spatial scales, and evaluating where predictions can be made safely. Methods are particularly useful for models fit using the 'tidymodels' framework. Methods include Moran's I ('Moran' (1950) <doi:10.2307/2332142>), Geary's C ('Geary' (1954) <doi:10.2307/2986645>), Getis-Ord's G ('Ord' and 'Getis' (1995) <doi:10.1111/j.1538-4632.1995.tb00912.x>), agreement coefficients from 'Ji' and Gallo (2006) (<doi: 10.14358/PERS.72.7.823>), agreement metrics from 'Willmott' (1981) (<doi: 10.1080/02723646.1981.10642213>) and 'Willmott' 'et' 'al'. (2012) (<doi: 10.1002/joc.2419>), an implementation of the area of applicability methodology from 'Meyer' and 'Pebesma' (2021) (<doi:10.1111/2041-210X.13650>), and an implementation of multi-scale assessment as described in 'Riemann' 'et' 'al'. (2010) (<doi:10.1016/j.rse.2010.05.010>).

Maintained by Michael Mahoney. Last updated 11 days ago.

spatial spatial-analysis tidymodels tidyverse

27.4 match 37 stars 6.93 score 19 scripts

rstudio

leaflet:Create Interactive Web Maps with the JavaScript 'Leaflet' Library

Create and customize interactive maps using the 'Leaflet' JavaScript library and the 'htmlwidgets' package. These maps can be used directly from the R console, from 'RStudio', in Shiny applications and R Markdown documents.

Maintained by Joe Cheng. Last updated 25 days ago.

gis leaflet-map spatial

11.0 match 821 stars 17.20 score 39k scripts 178 dependents

jeremygelb

geocmeans:Implementing Methods for Spatial Fuzzy Unsupervised Classification

Provides functions to apply spatial fuzzy unsupervised classification, visualize and interpret results. This method is well suited when the user wants to analyze data with a fuzzy clustering algorithm and to account for the spatial dimension of the dataset. In addition, indexes for estimating the spatial consistency and classification quality are proposed. The methods were originally proposed in the field of brain imagery (seed Cai and al. 2007 <doi:10.1016/j.patcog.2006.07.011> and Zaho and al. 2013 <doi:10.1016/j.dsp.2012.09.016>) and recently applied in geography (see Gelb and Apparicio <doi:10.4000/cybergeo.36414>).

Maintained by Jeremy Gelb. Last updated 4 months ago.

clustering cmeans fuzzy-classification-algorithms spatial-analysis spatial-fuzzy-cmeans unsupervised-learning cpp openmp

30.8 match 28 stars 6.10 score 90 scripts

bioc

SpaceMarkers:Spatial Interaction Markers

Spatial transcriptomic technologies have helped to resolve the connection between gene expression and the 2D orientation of tissues relative to each other. However, the limited single-cell resolution makes it difficult to highlight the most important molecular interactions in these tissues. SpaceMarkers, R/Bioconductor software, can help to find molecular interactions, by identifying genes associated with latent space interactions in spatial transcriptomics.

Maintained by Atul Deshpande. Last updated 11 days ago.

singlecell geneexpression software spatial transcriptomics

28.6 match 5 stars 6.55 score 21 scripts

ropensci

spatsoc:Group Animal Relocation Data by Spatial and Temporal Relationship

Detects spatial and temporal groups in GPS relocations (Robitaille et al. (2019) <doi:10.1111/2041-210X.13215>). It can be used to convert GPS relocations to gambit-of-the-group format to build proximity-based social networks In addition, the randomizations function provides data-stream randomization methods suitable for GPS data.

Maintained by Alec L. Robitaille. Last updated 2 months ago.

animal gps network social spatial

18.7 match 24 stars 9.97 score 145 scripts 3 dependents

mlr-org

mlr3spatial:Support for Spatial Objects Within the 'mlr3' Ecosystem

Extends the 'mlr3' ML framework with methods for spatial objects. Data storage and prediction are supported for packages 'terra', 'raster' and 'stars'.

Maintained by Marc Becker. Last updated 1 years ago.

mlr3 raster-prediction spatial spatial-modelling

27.5 match 43 stars 6.75 score 66 scripts

r-spatial

sftime:Classes and Methods for Simple Feature Objects that Have a Time Column

Classes and methods for spatial objects that have a registered time column, in particular for irregular spatiotemporal data. The time column can be of any type, but needs to be ordinal. Regularly laid out spatiotemporal data (vector or raster data cubes) are handled by package 'stars'.

Maintained by Henning Teickner. Last updated 1 months ago.

18.5 match 49 stars 9.99 score 27 scripts 60 dependents

bioc

SPOTlight:`SPOTlight`: Spatial Transcriptomics Deconvolution

`SPOTlight`provides a method to deconvolute spatial transcriptomics spots using a seeded NMF approach along with visualization tools to assess the results. Spatially resolved gene expression profiles are key to understand tissue organization and function. However, novel spatial transcriptomics (ST) profiling techniques lack single-cell resolution and require a combination with single-cell RNA sequencing (scRNA-seq) information to deconvolute the spatially indexed datasets. Leveraging the strengths of both data types, we developed SPOTlight, a computational tool that enables the integration of ST with scRNA-seq data to infer the location of cell types and states within a complex tissue. SPOTlight is centered around a seeded non-negative matrix factorization (NMF) regression, initialized using cell-type marker genes and non-negative least squares (NNLS) to subsequently deconvolute ST capture locations (spots).

Maintained by Marc Elosua-Bayes. Last updated 5 months ago.

singlecell spatial statisticalmethod

21.8 match 172 stars 8.37 score 170 scripts

bioc

imcRtools:Methods for imaging mass cytometry data analysis

This R package supports the handling and analysis of imaging mass cytometry and other highly multiplexed imaging data. The main functionality includes reading in single-cell data after image segmentation and measurement, data formatting to perform channel spillover correction and a number of spatial analysis approaches. First, cell-cell interactions are detected via spatial graph construction; these graphs can be visualized with cells representing nodes and interactions representing edges. Furthermore, per cell, its direct neighbours are summarized to allow spatial clustering. Per image/grouping level, interactions between types of cells are counted, averaged and compared against random permutations. In that way, types of cells that interact more (attraction) or less (avoidance) frequently than expected by chance are detected.

Maintained by Daniel Schulz. Last updated 5 months ago.

immunooncology singlecell spatial dataimport clustering imc single-cell

24.0 match 24 stars 7.58 score 126 scripts

spatlyu

HSAR:Hierarchical Spatial Autoregressive Model

A Hierarchical Spatial Autoregressive Model (HSAR), based on a Bayesian Markov Chain Monte Carlo (MCMC) algorithm (Dong and Harris (2014) <doi:10.1111/gean.12049>). The creation of this package was supported by the Economic and Social Research Council (ESRC) through the Applied Quantitative Methods Network: Phase II, grant number ES/K006460/1.

Maintained by Wenbo Lv. Last updated 3 months ago.

spatial-econometrics spatial-regression spatial-statistics openblas cpp openmp

32.6 match 8 stars 5.56 score 30 scripts

symbolixau

googleway:Accesses Google Maps APIs to Retrieve Data and Plot Maps

Provides a mechanism to plot a 'Google Map' from 'R' and overlay it with shapes and markers. Also provides access to 'Google Maps' APIs, including places, directions, roads, distances, geocoding, elevation and timezone.

Maintained by David Cooley. Last updated 7 months ago.

google-map google-maps google-maps-api google-maps-javascript-api spatial spatial-analysis

18.5 match 236 stars 9.67 score 536 scripts 2 dependents

biodiverse

spAbundance:Univariate and Multivariate Spatial Modeling of Species Abundance

Fits single-species (univariate) and multi-species (multivariate) non-spatial and spatial abundance models in a Bayesian framework using Markov Chain Monte Carlo (MCMC). Spatial models are fit using Nearest Neighbor Gaussian Processes (NNGPs). Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and Banerjee (2022) <doi:10.18637/jss.v103.i05>. Fits single-species and multi-species spatial and non-spatial versions of generalized linear mixed models (Gaussian, Poisson, Negative Binomial), N-mixture models (Royle 2004 <doi:10.1111/j.0006-341X.2004.00142.x>) and hierarchical distance sampling models (Royle, Dawson, Bates (2004) <doi:10.1890/03-3127>). Multi-species spatial models are fit using a spatial factor modeling approach with NNGPs for computational efficiency.

Maintained by Jeffrey Doser. Last updated 4 days ago.

openblas cpp openmp

29.0 match 17 stars 6.15 score 43 scripts 1 dependents

spatial-ews

spatialwarnings:Spatial Early Warning Signals of Ecosystem Degradation

Tools to compute and assess significance of early-warnings signals (EWS) of ecosystem degradation on raster data sets. EWS are spatial metrics derived from raster data -- e.g. spatial autocorrelation -- that increase before an ecosystem undergoes a non-linear transition (Genin et al. (2018) <doi:10.1111/2041-210X.13058>).

Maintained by Alexandre Genin. Last updated 7 months ago.

catastrophic critical ecology indicators points shifts space transitions cpp

33.2 match 15 stars 5.32 score 46 scripts

spatlyu

spEcula:Spatial Prediction Methods In R

Advanced spatial prediction methods based on various spatial relationships.

Maintained by Wenbo Lv. Last updated 9 months ago.

geoinformatics giscience spatial-analysis spatial-predictions spatial-statistics

35.0 match 21 stars 5.02 score 6 scripts

spatstat

spatstat.utils:Utility Functions for 'spatstat'

Contains utility functions for the 'spatstat' family of packages which may also be useful for other purposes.

Maintained by Adrian Baddeley. Last updated 14 days ago.

spatial-analysis spatial-data spatstat

15.0 match 5 stars 11.66 score 134 scripts 248 dependents

famuvie

breedR:Statistical Methods for Forest Genetic Resources Analysts

Statistical tools to build predictive models for the breeders community. It aims to assess the genetic value of individuals under a number of situations, including spatial autocorrelation, genetic/environment interaction and competition. It is under active development as part of the Trees4Future project, particularly developed having forest genetic trials in mind. But can be used for animals or other situations as well.

Maintained by Facundo Muñoz. Last updated 8 months ago.

32.1 match 33 stars 5.44 score 24 scripts

bioc

SpotSweeper:Spatially-aware quality control for spatial transcriptomics

Spatially-aware quality control (QC) software for both spot-level and artifact-level QC in spot-based spatial transcripomics, such as 10x Visium. These methods calculate local (nearest-neighbors) mean and variance of standard QC metrics (library size, unique genes, and mitochondrial percentage) to identify outliers spot and large technical artifacts.

Maintained by Michael Totty. Last updated 3 months ago.

software spatial transcriptomics qualitycontrol geneexpression bioconductor quality-control spatial-transcriptomics

26.0 match 5 stars 6.66 score 77 scripts

appelmar

gdalcubes:Earth Observation Data Cubes from Satellite Image Collections

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, plotting, and extraction from spatial and or spatiotemporal features. 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.

Maintained by Marius Appel. Last updated 9 days ago.

remote-sensing satellite-imagery spatial-analysis gdal netcdf cpp

21.2 match 124 stars 8.12 score 356 scripts

bioc

SPIAT:Spatial Image Analysis of Tissues

SPIAT (**Sp**atial **I**mage **A**nalysis of **T**issues) is an R package with a suite of data processing, quality control, visualization and data analysis tools. SPIAT is compatible with data generated from single-cell spatial proteomics platforms (e.g. OPAL, CODEX, MIBI, cellprofiler). SPIAT reads spatial data in the form of X and Y coordinates of cells, marker intensities and cell phenotypes. SPIAT includes six analysis modules that allow visualization, calculation of cell colocalization, categorization of the immune microenvironment relative to tumor areas, analysis of cellular neighborhoods, and the quantification of spatial heterogeneity, providing a comprehensive toolkit for spatial data analysis.

Maintained by Yuzhou Feng. Last updated 13 days ago.

biomedicalinformatics cellbiology spatial clustering dataimport immunooncology qualitycontrol singlecell software visualization

19.9 match 22 stars 8.59 score 69 scripts

spatstat

spatstat.linnet:Linear Networks Functionality of the 'spatstat' Family

Defines types of spatial data on a linear network and provides functionality for geometrical operations, data analysis and modelling of data on a linear network, in the 'spatstat' family of packages. Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks. Data types defined on a network include point patterns, pixel images, functions, and tessellations. Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. Random point patterns on a network can be generated using a variety of models.

Maintained by Adrian Baddeley. Last updated 2 months ago.

density-estimation heat-equation kernel-density-estimation network-analysis point-processes spatial-data-analysis statistical-analysis statistical-inference statistical-models

17.8 match 6 stars 9.58 score 35 scripts 42 dependents

accarniel

fsr:Handling Fuzzy Spatial Data

Support for fuzzy spatial objects, their operations, and fuzzy spatial inference models based on Spatial Plateau Algebra. It employs fuzzy set theory and fuzzy logic as foundation to deal with spatial fuzziness. It mainly implements underlying concepts defined in the following research papers: (i) "Spatial Plateau Algebra: An Executable Type System for Fuzzy Spatial Data Types" <doi:10.1109/FUZZ-IEEE.2018.8491565>; (ii) "A Systematic Approach to Creating Fuzzy Region Objects from Real Spatial Data Sets" <doi:10.1109/FUZZ-IEEE.2019.8858878>; (iii) "Spatial Data Types for Heterogeneously Structured Fuzzy Spatial Collections and Compositions" <doi:10.1109/FUZZ48607.2020.9177620>; (iv) "Fuzzy Inference on Fuzzy Spatial Objects (FIFUS) for Spatial Decision Support Systems" <doi:10.1109/FUZZ-IEEE.2017.8015707>; (v) "Evaluating Region Inference Methods by Using Fuzzy Spatial Inference Models" <doi:10.1109/FUZZ-IEEE55066.2022.9882658>.

Maintained by Anderson Carniel. Last updated 1 years ago.

fuzzy-inference-system fuzzy-logic fuzzy-spatial-data spatial-data spatial-data-science

40.6 match 10 stars 4.18 score

rozetasimonovska

SDPDmod:Spatial Dynamic Panel Data Modeling

Spatial model calculation for static and dynamic panel data models, weights matrix creation and Bayesian model comparison. Bayesian model comparison methods were described by 'LeSage' (2014) <doi:10.1016/j.spasta.2014.02.002>. The 'Lee'-'Yu' transformation approach is described in 'Yu', 'De Jong' and 'Lee' (2008) <doi:10.1016/j.jeconom.2008.08.002>, 'Lee' and 'Yu' (2010) <doi:10.1016/j.jeconom.2009.08.001> and 'Lee' and 'Yu' (2010) <doi:10.1017/S0266466609100099>.

Maintained by Rozeta Simonovska. Last updated 12 months ago.

33.9 match 5 stars 4.98 score 19 scripts

bioc

spicyR:Spatial analysis of in situ cytometry data

The spicyR package provides a framework for performing inference on changes in spatial relationships between pairs of cell types for cell-resolution spatial omics technologies. spicyR consists of three primary steps: (i) summarizing the degree of spatial localization between pairs of cell types for each image; (ii) modelling the variability in localization summary statistics as a function of cell counts and (iii) testing for changes in spatial localizations associated with a response variable.

Maintained by Ellis Patrick. Last updated 24 days ago.

singlecell cellbasedassays spatial

20.9 match 9 stars 8.02 score 57 scripts 1 dependents

eblondel

ows4R:Interface to OGC Web-Services (OWS)

Provides an Interface to Web-Services defined as standards by the Open Geospatial Consortium (OGC), including Web Feature Service (WFS) for vector data, Web Coverage Service (WCS), Catalogue Service (CSW) for ISO/OGC metadata, Web Processing Service (WPS) for data processes, and associated standards such as the common web-service specification (OWS) and OGC Filter Encoding. Partial support is provided for the Web Map Service (WMS). The purpose is to add support for additional OGC service standards such as Web Coverage Processing Service (WCPS), the Sensor Observation Service (SOS), or even new standard services emerging such OGC API or SensorThings.

Maintained by Emmanuel Blondel. Last updated 2 months ago.

catalogue-service csw dataaccess fes geospatial iso ogc ows sdi spatial spatial-data standard webfeatureservice wfs

18.5 match 38 stars 9.03 score 99 scripts 5 dependents

r-spatial

leafgl:High-Performance 'WebGl' Rendering for Package 'leaflet'

Provides bindings to the 'Leaflet.glify' JavaScript library which extends the 'leaflet' JavaScript library to render large data in the browser using 'WebGl'.

Maintained by Tim Appelhans. Last updated 5 months ago.

15.0 match 271 stars 10.63 score 157 scripts 27 dependents

murrayefford

secr:Spatially Explicit Capture-Recapture

Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.

Maintained by Murray Efford. Last updated 2 days ago.

cpp

15.7 match 3 stars 10.06 score 410 scripts 5 dependents

gamlss-dev

gamlss.spatial:Spatial Terms in Generalized Additive Models for Location Scale and Shape

The packages enables fitting Gaussian Markov Random Fields within the Generalized Additive Models for Location Scale and Shape algorithms.

Maintained by Fernanda De Bastiani. Last updated 1 years ago.

42.0 match 3.74 score 11 scripts

covaruber

sommer:Solving Mixed Model Equations in R

Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>; Jensen et al., 1997). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available.

Maintained by Giovanny Covarrubias-Pazaran. Last updated 2 days ago.

average-information mixed-models rcpparmadillo openblas cpp openmp

12.1 match 44 stars 12.63 score 300 scripts 10 dependents

r-spatial

qgisprocess:Use 'QGIS' Processing Algorithms

Provides seamless access to the 'QGIS' (<https://qgis.org>) processing toolbox using the standalone 'qgis_process' command-line utility. Both native and third-party (plugin) processing providers are supported. Beside referring data sources from file, also common objects from 'sf', 'terra' and 'stars' are supported. The native processing algorithms are documented by QGIS.org (2024) <https://docs.qgis.org/latest/en/docs/user_manual/processing_algs/>.

Maintained by Floris Vanderhaeghe. Last updated 10 days ago.

15.0 match 210 stars 10.05 score 175 scripts

diegommcc

SpatialDDLS:Deconvolution of Spatial Transcriptomics Data Based on Neural Networks

Deconvolution of spatial transcriptomics data based on neural networks and single-cell RNA-seq data. SpatialDDLS implements a workflow to create neural network models able to make accurate estimates of cell composition of spots from spatial transcriptomics data using deep learning and the meaningful information provided by single-cell RNA-seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> and Mañanes et al. (2024) <doi:10.1093/bioinformatics/btae072> to get an overview of the method and see some examples of its performance.

Maintained by Diego Mañanes. Last updated 5 months ago.

deconvolution deep-learning neural-network spatial-transcriptomics

30.9 match 5 stars 4.88 score 1 scripts

symbolixau

googlePolylines:Encoding Coordinates into 'Google' Polylines

Encodes simple feature ('sf') objects and coordinates, and decodes polylines using the 'Google' polyline encoding algorithm (<https://developers.google.com/maps/documentation/utilities/polylinealgorithm>).

Maintained by David Cooley. Last updated 15 days ago.

geospatial gis google-maps polyline-encoder r-spatial spatial cpp

18.5 match 18 stars 8.11 score 9 dependents

geocompx

geocompkg:Geocomputation with R Metapackage

Package supporting the book Geocomputation with R (\url{https://r.geocompx.org}). The packages in the Imports are required to build the first chapter of the book. The packages in Suggests are required for Part II and III.

Maintained by Jakub Nowosad. Last updated 6 months ago.

geocomputation spatial

24.4 match 21 stars 6.10 score 2 scripts

r-spatial

leafpop:Include Tables, Images and Graphs in Leaflet Pop-Ups

Creates 'HTML' strings to embed tables, images or graphs in pop-ups of interactive maps created with packages like 'leaflet' or 'mapview'. Handles local images located on the file system or via remote URL. Handles graphs created with 'lattice' or 'ggplot2' as well as interactive plots created with 'htmlwidgets'.

Maintained by Tim Appelhans. Last updated 6 months ago.

15.0 match 114 stars 9.87 score 458 scripts 27 dependents

biorgeo

bioregion:Comparison of Bioregionalisation Methods

The main purpose of this package is to propose a transparent methodological framework to compare bioregionalisation methods based on hierarchical and non-hierarchical clustering algorithms (Kreft & Jetz (2010) <doi:10.1111/j.1365-2699.2010.02375.x>) and network algorithms (Lenormand et al. (2019) <doi:10.1002/ece3.4718> and Leroy et al. (2019) <doi:10.1111/jbi.13674>).

Maintained by Maxime Lenormand. Last updated 23 days ago.

biogeography bioregion bioregionalization cpp

23.6 match 7 stars 6.27 score 11 scripts

r-spatial

mapedit:Interactive Editing of Spatial Data in R

Suite of interactive functions and helpers for selecting and editing geospatial data.

Maintained by Tim Appelhans. Last updated 3 years ago.

18.0 match 218 stars 8.20 score 410 scripts 1 dependents

rspatial

raster:Geographic Data Analysis and Modeling

Reading, writing, manipulating, analyzing and modeling of spatial data. This package has been superseded by the "terra" package <https://CRAN.R-project.org/package=terra>.

Maintained by Robert J. Hijmans. Last updated 20 hours ago.

cpp

8.5 match 163 stars 17.23 score 58k scripts 562 dependents

r-spatial

link2GI:Linking Geographic Information Systems, Remote Sensing and Other Command Line Tools

Functions and tools for using open GIS and remote sensing command-line interfaces in a reproducible environment.

Maintained by Chris Reudenbach. Last updated 4 months ago.

16.3 match 26 stars 8.99 score 78 scripts 1 dependents

nowosad

motif:Local Pattern Analysis

Describes spatial patterns of categorical raster data for any defined regular and irregular areas. Patterns are described quantitatively using built-in signatures based on co-occurrence matrices but also allows for any user-defined functions. It enables spatial analysis such as search, change detection, and clustering to be performed on spatial patterns (Nowosad (2021) <doi:10.1007/s10980-020-01135-0>).

Maintained by Jakub Nowosad. Last updated 7 months ago.

categorical-raster global-ecology landscape-ecology spatial cpp

19.5 match 63 stars 7.48 score 48 scripts

levisc8

spind:Spatial Methods and Indices

Functions for spatial methods based on generalized estimating equations (GEE) and wavelet-revised methods (WRM), functions for scaling by wavelet multiresolution regression (WMRR), conducting multi-model inference, and stepwise model selection. Further, contains functions for spatially corrected model accuracy measures.

Maintained by Sam Levin. Last updated 1 years ago.

models r-spatial spatial

30.1 match 3 stars 4.84 score 46 scripts

bioc

standR:Spatial transcriptome analyses of Nanostring's DSP data in R

standR is an user-friendly R package providing functions to assist conducting good-practice analysis of Nanostring's GeoMX DSP data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. standR allows data inspection, quality control, normalization, batch correction and evaluation with informative visualizations.

Maintained by Ning Liu. Last updated 2 months ago.

spatial transcriptomics geneexpression differentialexpression qualitycontrol normalization experimenthubsoftware

19.5 match 18 stars 7.39 score 45 scripts

huizezhang-sherry

cubble:A Vector Spatio-Temporal Data Structure for Data Analysis

A spatiotemperal data object in a relational data structure to separate the recording of time variant/ invariant variables. See the Journal of Statistical Software reference: <doi:10.18637/jss.v110.i07>.

Maintained by H. Sherry Zhang. Last updated 6 months ago.

15.9 match 57 stars 9.07 score 83 scripts

bioc

SpaNorm:Spatially-aware normalisation for spatial transcriptomics data

This package implements the spatially aware library size normalisation algorithm, SpaNorm. SpaNorm normalises out library size effects while retaining biology through the modelling of smooth functions for each effect. Normalisation is performed in a gene- and cell-/spot- specific manner, yielding library size adjusted data.

Maintained by Dharmesh D. Bhuva. Last updated 5 months ago.

software geneexpression transcriptomics spatial cellbiology

24.2 match 9 stars 5.95 score 3 scripts

bioc

spatialFDA:A Tool for Spatial Multi-sample Comparisons

spatialFDA is a package to calculate spatial statistics metrics. The package takes a SpatialExperiment object and calculates spatial statistics metrics using the package spatstat. Then it compares the resulting functions across samples/conditions using functional additive models as implemented in the package refund. Furthermore, it provides exploratory visualisations using functional principal component analysis, as well implemented in refund.

Maintained by Martin Emons. Last updated 1 months ago.

software spatial transcriptomics

27.7 match 3 stars 5.18 score 6 scripts

bioc

sosta:A package for the analysis of anatomical tissue structures in spatial omics data

sosta (Spatial Omics STructure Analysis) is a package for analyzing spatial omics data to explore tissue organization at the anatomical structure level. It reconstructs morphologically relevant structures based on molecular features or cell types. It further calculates a range of structural and shape metrics to quantitatively describe tissue architecture. The package is designed to integrate with other packages for the analysis of spatial (omics) data.

Maintained by Samuel Gunz. Last updated 7 days ago.

software spatial transcriptomics visualization

25.1 match 1 stars 5.68 score 2 scripts 1 dependents

giorgilancs

PrevMap:Geostatistical Modelling of Spatially Referenced Prevalence Data

Provides functions for both likelihood-based and Bayesian analysis of spatially referenced prevalence data. For a tutorial on the use of the R package, see Giorgi and Diggle (2017) <doi:10.18637/jss.v078.i08>.

Maintained by Emanuele Giorgi. Last updated 2 years ago.

32.6 match 4.36 score 46 scripts

martinschobben

oceanexplorer:Explore Our Planet's Oceans with NOAA

Provides tools for easy exploration of the world ocean atlas of the US agency National Oceanic and Atmospheric Administration (NOAA). It includes functions to extract NetCDF data from the repository and code to visualize several physical and chemical parameters of the ocean. A Shiny app further allows interactive exploration of the data. The methods for data collecting and quality checks are described in several papers, which can be found here: <https://www.ncei.noaa.gov/products/world-ocean-atlas>.

Maintained by Martin Schobben. Last updated 1 years ago.

earth earth-observation earth-science noaa noaa-data ocean ocean-sciences oceanography open-data shiny spatial spatial-analysis spatial-data

27.8 match 9 stars 5.01 score 23 scripts

ausgis

geosimilarity:Geographically Optimal Similarity

Understanding spatial association is essential for spatial statistical inference, including factor exploration and spatial prediction. Geographically optimal similarity (GOS) model is an effective method for spatial prediction, as described in Yongze Song (2022) <doi:10.1007/s11004-022-10036-8>. GOS was developed based on the geographical similarity principle, as described in Axing Zhu (2018) <doi:10.1080/19475683.2018.1534890>. GOS has advantages in more accurate spatial prediction using fewer samples and critically reduced prediction uncertainty.

Maintained by Wenbo Lv. Last updated 2 months ago.

geoinformatics geospatial-analytics spatial-predictions spatial-statistics

25.7 match 6 stars 5.38 score 5 scripts

bioc

pRoloc:A unifying bioinformatics framework for spatial proteomics

The pRoloc package implements machine learning and visualisation methods for the analysis and interogation of quantitiative mass spectrometry data to reliably infer protein sub-cellular localisation.

Maintained by Lisa Breckels. Last updated 1 months ago.

immunooncology proteomics massspectrometry classification clustering qualitycontrol bioconductor proteomics-data spatial-proteomics visualisation openblas cpp

15.9 match 15 stars 8.64 score 101 scripts 2 dependents

r-spatialecology

landscapemetrics:Landscape Metrics for Categorical Map Patterns

Calculates landscape metrics for categorical landscape patterns in a tidy workflow. 'landscapemetrics' reimplements the most common metrics from 'FRAGSTATS' (<https://www.fragstats.org/>) and new ones from the current literature on landscape metrics. This package supports 'terra' SpatRaster objects as input arguments. It further provides utility functions to visualize patches, select metrics and building blocks to develop new metrics.

Maintained by Maximilian H.K. Hesselbarth. Last updated 2 months ago.

landscape-ecology landscape-metrics raster spatial cpp

11.0 match 240 stars 12.47 score 584 scripts 4 dependents

bioc

lisaClust:lisaClust: Clustering of Local Indicators of Spatial Association

lisaClust provides a series of functions to identify and visualise regions of tissue where spatial associations between cell-types is similar. This package can be used to provide a high-level summary of cell-type colocalization in multiplexed imaging data that has been segmented at a single-cell resolution.

Maintained by Ellis Patrick. Last updated 4 months ago.

singlecell cellbasedassays spatial

20.7 match 3 stars 6.64 score 48 scripts

bioc

tpSVG:Thin plate models to detect spatially variable genes

The goal of `tpSVG` is to detect and visualize spatial variation in the gene expression for spatially resolved transcriptomics data analysis. Specifically, `tpSVG` introduces a family of count-based models, with generalizable parametric assumptions such as Poisson distribution or negative binomial distribution. In addition, comparing to currently available count-based model for spatially resolved data analysis, the `tpSVG` models improves computational time, and hence greatly improves the applicability of count-based models in SRT data analysis.

Maintained by Boyi Guo. Last updated 5 months ago.

spatial transcriptomics geneexpression software statisticalmethod dimensionreduction regression preprocessing spatially-resolve spatially-variable-genes

31.8 match 2 stars 4.30 score 2 scripts

nowosad

spDataLarge:Large datasets for spatial analysis

Large datasets for spatial analysis. The data from this package could be retrived using the spData package.

Maintained by Jakub Nowosad. Last updated 6 months ago.

raster sf spatial spatial-data

22.2 match 27 stars 6.15 score 1.2k scripts 1 dependents

dmuraka

spmoran:Fast Spatial and Spatio-Temporal Regression using Moran Eigenvectors

A collection of functions for estimating spatial and spatio-temporal regression models. Moran eigenvectors are used as spatial basis functions to efficiently approximate spatially dependent Gaussian processes (i.e., random effects eigenvector spatial filtering; see Murakami and Griffith 2015 <doi: 10.1007/s10109-015-0213-7>). The implemented models include linear regression with residual spatial dependence, spatially/spatio-temporally varying coefficient models (Murakami et al., 2017, 2024; <doi:10.1016/j.spasta.2016.12.001>,<doi:10.48550/arXiv.2410.07229>), spatially filtered unconditional quantile regression (Murakami and Seya, 2019 <doi:10.1002/env.2556>), Gaussian and non-Gaussian spatial mixed models through compositionally-warping (Murakami et al. 2021, <doi:10.1016/j.spasta.2021.100520>).

Maintained by Daisuke Murakami. Last updated 4 months ago.

32.9 match 4.12 score 37 scripts

ropensci

geojsonio:Convert Data from and to 'GeoJSON' or 'TopoJSON'

Convert data to 'GeoJSON' or 'TopoJSON' from various R classes, including vectors, lists, data frames, shape files, and spatial classes. 'geojsonio' does not aim to replace packages like 'sp', 'rgdal', 'rgeos', but rather aims to be a high level client to simplify conversions of data from and to 'GeoJSON' and 'TopoJSON'.

Maintained by Michael Mahoney. Last updated 1 years ago.

geojson topojson geospatial conversion data input-output io

12.5 match 151 stars 10.83 score 2.9k scripts 13 dependents

r-spatial

leafsync:Small Multiples for Leaflet Web Maps

Create small multiples of several leaflet web maps with (optional) synchronised panning and zooming control. When syncing is enabled all maps respond to mouse actions on one map. This allows side-by-side comparisons of different attributes of the same geometries. Syncing can be adjusted so that any combination of maps can be synchronised.

Maintained by Tim Appelhans. Last updated 3 years ago.

15.0 match 36 stars 8.98 score 326 scripts 28 dependents

prioritizr

prioritizr:Systematic Conservation Prioritization in R

Systematic conservation prioritization using mixed integer linear programming (MILP). It provides a flexible interface for building and solving conservation planning problems. Once built, conservation planning problems can be solved using a variety of commercial and open-source exact algorithm solvers. By using exact algorithm solvers, solutions can be generated that are guaranteed to be optimal (or within a pre-specified optimality gap). Furthermore, conservation problems can be constructed to optimize the spatial allocation of different management actions or zones, meaning that conservation practitioners can identify solutions that benefit multiple stakeholders. To solve large-scale or complex conservation planning problems, users should install the Gurobi optimization software (available from <https://www.gurobi.com/>) and the 'gurobi' R package (see Gurobi Installation Guide vignette for details). Users can also install the IBM CPLEX software (<https://www.ibm.com/products/ilog-cplex-optimization-studio/cplex-optimizer>) and the 'cplexAPI' R package (available at <https://github.com/cran/cplexAPI>). Additionally, the 'rcbc' R package (available at <https://github.com/dirkschumacher/rcbc>) can be used to generate solutions using the CBC optimization software (<https://github.com/coin-or/Cbc>). For further details, see Hanson et al. (2025) <doi:10.1111/cobi.14376>.

Maintained by Richard Schuster. Last updated 1 days ago.

biodiversity conservation conservation-planner optimization prioritization solver spatial cpp

11.5 match 124 stars 11.71 score 584 scripts 2 dependents

mhahsler

dbscan:Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms

A fast reimplementation of several density-based algorithms of the DBSCAN family. Includes the clustering algorithms DBSCAN (density-based spatial clustering of applications with noise) and HDBSCAN (hierarchical DBSCAN), the ordering algorithm OPTICS (ordering points to identify the clustering structure), shared nearest neighbor clustering, and the outlier detection algorithms LOF (local outlier factor) and GLOSH (global-local outlier score from hierarchies). The implementations use the kd-tree data structure (from library ANN) for faster k-nearest neighbor search. An R interface to fast kNN and fixed-radius NN search is also provided. Hahsler, Piekenbrock and Doran (2019) <doi:10.18637/jss.v091.i01>.

Maintained by Michael Hahsler. Last updated 2 months ago.

clustering dbscan density-based-clustering hdbscan lof optics cpp

8.6 match 324 stars 15.60 score 1.6k scripts 85 dependents

bioc

smoppix:Analyze Single Molecule Spatial Omics Data Using the Probabilistic Index

Test for univariate and bivariate spatial patterns in spatial omics data with single-molecule resolution. The tests implemented allow for analysis of nested designs and are automatically calibrated to different biological specimens. Tests for aggregation, colocalization, gradients and vicinity to cell edge or centroid are provided.

Maintained by Stijn Hawinkel. Last updated 1 months ago.

transcriptomics spatial singlecell cpp

26.3 match 1 stars 5.10 score 4 scripts

r-a-dobson

dynamicSDM:Species Distribution and Abundance Modelling at High Spatio-Temporal Resolution

A collection of novel tools for generating species distribution and abundance models (SDM) that are dynamic through both space and time. These highly flexible functions incorporate spatial and temporal aspects across key SDM stages; including when cleaning and filtering species occurrence data, generating pseudo-absence records, assessing and correcting sampling biases and autocorrelation, extracting explanatory variables and projecting distribution patterns. Throughout, functions utilise Google Earth Engine and Google Drive to minimise the computing power and storage demands associated with species distribution modelling at high spatio-temporal resolution.

Maintained by Rachel Dobson. Last updated 1 months ago.

dynamicsdm google-earth-engine googledrive sdm spatiotemporal spatiotemporal-data-analysis spatiotemporal-forecasting species-distribution-modelling species-distributions

21.6 match 6 stars 6.16 score 20 scripts

briencj

asremlPlus:Augments 'ASReml-R' in Fitting Mixed Models and Packages Generally in Exploring Prediction Differences

Assists in automating the selection of terms to include in mixed models when 'asreml' is used to fit the models. Procedures are available for choosing models that conform to the hierarchy or marginality principle, for fitting and choosing between two-dimensional spatial models using correlation, natural cubic smoothing spline and P-spline models. A history of the fitting of a sequence of models is kept in a data frame. Also used to compute functions and contrasts of, to investigate differences between and to plot predictions obtained using any model fitting function. The content falls into the following natural groupings: (i) Data, (ii) Model modification functions, (iii) Model selection and description functions, (iv) Model diagnostics and simulation functions, (v) Prediction production and presentation functions, (vi) Response transformation functions, (vii) Object manipulation functions, and (viii) Miscellaneous functions (for further details see 'asremlPlus-package' in help). The 'asreml' package provides a computationally efficient algorithm for fitting a wide range of linear mixed models using Residual Maximum Likelihood. It is a commercial package and a license for it can be purchased from 'VSNi' <https://vsni.co.uk/> as 'asreml-R', who will supply a zip file for local installation/updating (see <https://asreml.kb.vsni.co.uk/>). It is not needed for functions that are methods for 'alldiffs' and 'data.frame' objects. The package 'asremPlus' can also be installed from <http://chris.brien.name/rpackages/>.

Maintained by Chris Brien. Last updated 1 months ago.

asreml mixed-models

14.2 match 19 stars 9.37 score 200 scripts

bioc

SpatialDecon:Deconvolution of mixed cells from spatial and/or bulk gene expression data

Using spatial or bulk gene expression data, estimates abundance of mixed cell types within each observation. Based on "Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data", Danaher (2022). Designed for use with the NanoString GeoMx platform, but applicable to any gene expression data.

Maintained by Maddy Griswold. Last updated 5 months ago.

immunooncology featureextraction geneexpression transcriptomics spatial

17.9 match 37 stars 7.41 score 58 scripts

pik-piam

magclass:Data Class and Tools for Handling Spatial-Temporal Data

Data class for increased interoperability working with spatial-temporal data together with corresponding functions and methods (conversions, basic calculations and basic data manipulation). The class distinguishes between spatial, temporal and other dimensions to facilitate the development and interoperability of tools build for it. Additional features are name-based addressing of data and internal consistency checks (e.g. checking for the right data order in calculations).

Maintained by Jan Philipp Dietrich. Last updated 22 days ago.

11.8 match 5 stars 11.16 score 412 scripts 56 dependents

bioc

BayesSpace:Clustering and Resolution Enhancement of Spatial Transcriptomes

Tools for clustering and enhancing the resolution of spatial gene expression experiments. BayesSpace clusters a low-dimensional representation of the gene expression matrix, incorporating a spatial prior to encourage neighboring spots to cluster together. The method can enhance the resolution of the low-dimensional representation into "sub-spots", for which features such as gene expression or cell type composition can be imputed.

Maintained by Matt Stone. Last updated 5 months ago.

software clustering transcriptomics geneexpression singlecell immunooncology dataimport openblas cpp openmp

14.8 match 123 stars 8.89 score 278 scripts 1 dependents

span-18

spStack:Bayesian Geostatistics Using Predictive Stacking

Fits Bayesian hierarchical spatial process models for point-referenced Gaussian, Poisson, binomial, and binary data using stacking of predictive densities. It involves sampling from analytically available posterior distributions conditional upon some candidate values of the spatial process parameters and, subsequently assimilate inference from these individual posterior distributions using Bayesian predictive stacking. Our algorithm is highly parallelizable and hence, much faster than traditional Markov chain Monte Carlo algorithms while delivering competitive predictive performance. See Zhang, Tang, and Banerjee (2024) <doi:10.48550/arXiv.2304.12414>, and, Pan, Zhang, Bradley, and Banerjee (2024) <doi:10.48550/arXiv.2406.04655> for details.

Maintained by Soumyakanti Pan. Last updated 17 hours ago.

openblas cpp

25.9 match 4.98 score 6 scripts

adamlilith

enmSdmX:Species Distribution Modeling and Ecological Niche Modeling

Implements species distribution modeling and ecological niche modeling, including: bias correction, spatial cross-validation, model evaluation, raster interpolation, biotic "velocity" (speed and direction of movement of a "mass" represented by a raster), interpolating across a time series of rasters, and use of spatially imprecise records. The heart of the package is a set of "training" functions which automatically optimize model complexity based number of available occurrences. These algorithms include MaxEnt, MaxNet, boosted regression trees/gradient boosting machines, generalized additive models, generalized linear models, natural splines, and random forests. To enhance interoperability with other modeling packages, no new classes are created. The package works with 'PROJ6' geodetic objects and coordinate reference systems.

Maintained by Adam B. Smith. Last updated 1 months ago.

bias-correction biogeography ecological-niche-modeling ecological-niche-modelling niche-modeling niche-modelling species-distribution-modeling openjdk

23.1 match 25 stars 5.57 score 37 scripts

sebastien-plutniak

archeofrag:Spatial Analysis in Archaeology from Refitting Fragments

Methods to analyse spatial units in archaeology from the relationships between refitting fragmented objects scattered in these units (e.g. stratigraphic layers). Graphs are used to model archaeological observations. The package is mainly based on the 'igraph' package for graph analysis. Functions can: 1) create, manipulate, and simulate fragmentation graphs, 2) measure the cohesion and admixture of archaeological spatial units, and 3) characterise the topology of a specific set of refitting relationships. Empirical datasets are provided as examples. Documentation about 'archeofrag' is provided by the vignette included in this package, by the accompanying scientific papers: Plutniak (2021, Journal of Archaeological Science, <doi:10.1016/j.jas.2021.105501>) and Plutniak (2022, Journal of Open Source Software, <doi:10.21105/joss.04335>). This package is complemented by a companion GUI application available at <https://analytics.huma-num.fr/Sebastien.Plutniak/archeofrag/>.

Maintained by Sebastien Plutniak. Last updated 1 days ago.

archaeological-objects archaeological-science archaeology fragmentation network-analysis

19.7 match 20 stars 6.48 score 10 scripts 1 dependents

highamm

sptotal:Predicting Totals and Weighted Sums from Spatial Data

Performs predictions of totals and weighted sums, or finite population block kriging, on spatial data using the methods in Ver Hoef (2008) <doi:10.1007/s10651-007-0035-y>. The primary outputs are an estimate of the total, mean, or weighted sum in the region, an estimated prediction variance, and a plot of the predicted and observed values. This is useful primarily to users with ecological data that are counts or densities measured on some sites in a finite area of interest. Spatial prediction for the total count or average density in the entire region can then be done using the functions in this package.

Maintained by Matt Higham. Last updated 8 months ago.

25.8 match 4 stars 4.90 score 10 scripts

bioc

nnSVG:Scalable identification of spatially variable genes in spatially-resolved transcriptomics data

Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data. The method is based on nearest-neighbor Gaussian processes and uses the BRISC algorithm for model fitting and parameter estimation. Allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. Scales linearly with the number of spatial locations and can be applied to datasets containing thousands or more spatial locations.

Maintained by Lukas M. Weber. Last updated 1 months ago.

spatial singlecell transcriptomics geneexpression preprocessing

16.6 match 17 stars 7.57 score 183 scripts 1 dependents

bafuentes

rassta:Raster-Based Spatial Stratification Algorithms

Algorithms for the spatial stratification of landscapes, sampling and modeling of spatially-varying phenomena. These algorithms offer a simple framework for the stratification of geographic space based on raster layers representing landscape factors and/or factor scales. The stratification process follows a hierarchical approach, which is based on first level units (i.e., classification units) and second-level units (i.e., stratification units). Nonparametric techniques allow to measure the correspondence between the geographic space and the landscape configuration represented by the units. These correspondence metrics are useful to define sampling schemes and to model the spatial variability of environmental phenomena. The theoretical background of the algorithms and code examples are presented in Fuentes, Dorantes, and Tipton (2021). <doi:10.31223/X50S57>.

Maintained by Bryan A. Fuentes. Last updated 3 years ago.

ecology geoinformatics hierarchical modeling sampling spatial

21.1 match 16 stars 5.96 score 19 scripts

sebastien-plutniak

archeoViz:Visualisation, Exploration, and Web Communication of Archaeological Spatial Data

An R 'Shiny' application for visual and statistical exploration and web communication of archaeological spatial data, either remains or sites. It offers interactive 3D and 2D visualisations (cross sections and maps of remains, timeline of the work made in a site) which can be exported in SVG and HTML formats. It performs simple spatial statistics (convex hull, regression surfaces, 2D kernel density estimation) and allows exporting data to other online applications for more complex methods. 'archeoViz' can be used offline locally or deployed on a server, either with interactive input of data or with a static data set. Example is provided at <https://analytics.huma-num.fr/archeoviz/en>.

Maintained by Sebastien Plutniak. Last updated 2 months ago.

archaeology archeology data-visualization

17.4 match 19 stars 7.23 score 6 scripts

stscl

sshicm:Information Consistency-Based Measures for Spatial Stratified Heterogeneity

Spatial stratified heterogeneity (SSH) denotes the coexistence of within-strata homogeneity and between-strata heterogeneity. Information consistency-based methods provide a rigorous approach to quantify SSH and evaluate its role in spatial processes, grounded in principles of geographical stratification and information theory (Bai, H. et al. (2023) <doi:10.1080/24694452.2023.2223700>; Wang, J. et al. (2024) <doi:10.1080/24694452.2023.2289982>).

Maintained by Wenbo Lv. Last updated 3 months ago.

geoinformatics geospatial-analysis information-theory spatial-statistics spatial-stratified-heterogeneity cpp

26.8 match 3 stars 4.65 score 2 scripts

babaknaimi

elsa:Entropy-Based Local Indicator of Spatial Association

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>.

Maintained by Babak Naimi. Last updated 1 years ago.

23.8 match 14 stars 5.23 score 24 scripts

faosorios

SpatialPack:Tools for Assessment the Association Between Two Spatial Processes

Tools to assess the association between two spatial processes. Currently, several methodologies are implemented: A modified t-test to perform hypothesis testing about the independence between the processes, a suitable nonparametric correlation coefficient, the codispersion coefficient, and an F test for assessing the multiple correlation between one spatial process and several others. Functions for image processing and computing the spatial association between images are also provided. Functions contained in the package are intended to accompany Vallejos, R., Osorio, F., Bevilacqua, M. (2020). Spatial Relationships Between Two Georeferenced Variables: With Applications in R. Springer, Cham <doi:10.1007/978-3-030-56681-4>.

Maintained by Felipe Osorio. Last updated 8 days ago.

codispersion-coefficient modified-t-test spatial-association spatial-processes ssim structural-similarity tjostheim-coefficient fortran

21.2 match 7 stars 5.88 score 73 scripts 1 dependents

alexisvdb

singleCellHaystack:A Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data

One key exploratory analysis step in single-cell genomics data analysis is the prediction of features with different activity levels. For example, we want to predict differentially expressed genes (DEGs) in single-cell RNA-seq data, spatial DEGs in spatial transcriptomics data, or differentially accessible regions (DARs) in single-cell ATAC-seq data. 'singleCellHaystack' predicts differentially active features in single cell omics datasets without relying on the clustering of cells into arbitrary clusters. 'singleCellHaystack' uses Kullback-Leibler divergence to find features (e.g., genes, genomic regions, etc) that are active in subsets of cells that are non-randomly positioned inside an input space (such as 1D trajectories, 2D tissue sections, multi-dimensional embeddings, etc). For the theoretical background of 'singleCellHaystack' we refer to our original paper Vandenbon and Diez (Nature Communications, 2020) <doi:10.1038/s41467-020-17900-3> and our update Vandenbon and Diez (Scientific Reports, 2023) <doi:10.1038/s41598-023-38965-2>.

Maintained by Alexis Vandenbon. Last updated 1 years ago.

bioinformatics cite-seq pseudotime scatac-seq single-cell spatial-proteomics spatial-transcriptomics transcriptomics

18.5 match 81 stars 6.71 score 64 scripts

iiasa

ibis.iSDM:Modelling framework for integrated biodiversity distribution scenarios

Integrated framework of modelling the distribution of species and ecosystems in a suitability framing. This package allows the estimation of integrated species distribution models (iSDM) based on several sources of evidence and provided presence-only and presence-absence datasets. It makes heavy use of point-process models for estimating habitat suitability and allows to include spatial latent effects and priors in the estimation. To do so 'ibis.iSDM' supports a number of engines for Bayesian and more non-parametric machine learning estimation. Further, the 'ibis.iSDM' is specifically customized to support spatial-temporal projections of habitat suitability into the future.

Maintained by Martin Jung. Last updated 5 months ago.

bayesian biodiversity integrated-framework poisson-process scenarios sdm spatial-grain spatial-predictions species-distribution-modelling

28.3 match 21 stars 4.36 score 12 scripts 1 dependents

16eagle

basemaps:Accessing Spatial Basemaps in R

A lightweight package to access spatial basemaps from open sources such as 'OpenStreetMap', 'Carto', 'Mapbox' and others in R.

Maintained by Jakob Schwalb-Willmann. Last updated 4 months ago.

basemaps carto esri mapbox maptiler openstreetmap osm spatial stadia stamen thunderforest

16.7 match 60 stars 7.38 score 307 scripts

tobiste

tectonicr:Analyzing the Orientation of Maximum Horizontal Stress

Models the direction of the maximum horizontal stress using relative plate motion parameters. Statistical algorithms to evaluate the modeling results compared with the observed data. Provides plots to visualize the results. Methods described in Stephan et al. (2023) <doi:10.1038/s41598-023-42433-2> and Wdowinski (1998) <doi:10.1016/S0079-1946(98)00091-3>.

Maintained by Tobias Stephan. Last updated 3 days ago.

geology structural-geology tectonics

16.8 match 7 stars 7.27 score 33 scripts

urbananalyst

dodgr:Distances on Directed Graphs

Distances on dual-weighted directed graphs using priority-queue shortest paths (Padgham (2019) <doi:10.32866/6945>). Weighted directed graphs have weights from A to B which may differ from those from B to A. Dual-weighted directed graphs have two sets of such weights. A canonical example is a street network to be used for routing in which routes are calculated by weighting distances according to the type of way and mode of transport, yet lengths of routes must be calculated from direct distances.

Maintained by Mark Padgham. Last updated 10 hours ago.

distance openstreetmap router shortest-paths street-networks cpp

10.6 match 129 stars 11.52 score 229 scripts 4 dependents

ericmarcon

dbmss:Distance-Based Measures of Spatial Structures

Simple computation of spatial statistic functions of distance to characterize the spatial structures of mapped objects, following Marcon, Traissac, Puech, and Lang (2015) <doi:10.18637/jss.v067.c03>. Includes classical functions (Ripley's K and others) and more recent ones used by spatial economists (Duranton and Overman's Kd, Marcon and Puech's M). Relies on 'spatstat' for some core calculation.

Maintained by Eric Marcon. Last updated 1 months ago.

concentration economic-geography spatial-structures specialization cpp

18.5 match 9 stars 6.53 score 42 scripts 1 dependents

jefworks-lab

scatterbar:Scattered Stacked Bar Chart Plots

Provides a powerful and flexible tool for visualizing proportional data across spatially resolved contexts. By combining the concepts of scatter plots and stacked bar charts, `scatterbar` allows users to create scattered bar chart plots, which effectively display the proportions of different categories at each (x, y) location. This visualization is particularly useful for applications where understanding the distribution of categories across spatial coordinates is essential. This package features automatic determination of optimal scaling factors based on data, customizable scaling and padding options for both x and y axes, flexibility to specify custom colors for each category, options to customize the legend title, and integration with `ggplot2` for robust and high-quality visualizations. For more details, see Velazquez et al. (2024) <doi:10.1101/2024.08.14.606810>.

Maintained by Dee Velazquez. Last updated 26 days ago.

data-visualization spatial-analysis spatial-data-analysis spatial-transcriptomics

25.6 match 6 stars 4.65 score 15 scripts

mapme-initiative

mapme.biodiversity:Efficient Monitoring of Global Biodiversity Portfolios

Biodiversity areas, especially primary forest, serve a multitude of functions for local economy, regional functionality of the ecosystems as well as the global health of our planet. Recently, adverse changes in human land use practices and climatic responses to increased greenhouse gas emissions, put these biodiversity areas under a variety of different threats. The present package helps to analyse a number of biodiversity indicators based on freely available geographical datasets. It supports computational efficient routines that allow the analysis of potentially global biodiversity portfolios. The primary use case of the package is to support evidence based reporting of an organization's effort to protect biodiversity areas under threat and to identify regions were intervention is most duly needed.

Maintained by Darius A. Görgen. Last updated 11 hours ago.

environment eo gis mapme spatial sustainability

12.8 match 35 stars 9.24 score 287 scripts

paul-buerkner

brms:Bayesian Regression Models using 'Stan'

Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Maintained by Paul-Christian Bürkner. Last updated 2 days ago.

bayesian-inference brms multilevel-models stan statistical-models

7.1 match 1.3k stars 16.64 score 13k scripts 35 dependents

ropengov

giscoR:Download Map Data from GISCO API - Eurostat

Tools to download data from the GISCO (Geographic Information System of the Commission) Eurostat database <https://ec.europa.eu/eurostat/web/gisco>. Global and European map data available. This package is in no way officially related to or endorsed by Eurostat.

Maintained by Diego Hernangómez. Last updated 2 days ago.

ropengov spatial api-wrapper eurostat gisco thematic-maps eurostat-data ggplot2 gis

11.0 match 75 stars 10.70 score 424 scripts 5 dependents

ropensci

geojson:Classes for 'GeoJSON'

Classes for 'GeoJSON' to make working with 'GeoJSON' easier. Includes S3 classes for 'GeoJSON' classes with brief summary output, and a few methods such as extracting and adding bounding boxes, properties, and coordinate reference systems; working with newline delimited 'GeoJSON'; and serializing to/from 'Geobuf' binary 'GeoJSON' format.

Maintained by Michael Sumner. Last updated 2 years ago.

geojson geospatial conversion data input-output bbox polygon geobuf crs ndgeojson spatial

11.0 match 32 stars 10.56 score 166 scripts 14 dependents

eblondel

cleangeo:Cleaning Geometries from Spatial Objects

Provides a set of utility tools to inspect spatial objects, facilitate handling and reporting of topology errors and geometry validity issue with sp objects. Finally, it provides a geometry cleaner that will fix all geometry problems, and eliminate (at least reduce) the likelihood of having issues when doing spatial data processing.

Maintained by Emmanuel Blondel. Last updated 2 years ago.

cleaning cleaning-geometries gis sp spatial

16.8 match 45 stars 6.82 score 99 scripts 1 dependents

usepa

spsurvey:Spatial Sampling Design and Analysis

A design-based approach to statistical inference, with a focus on spatial data. Spatially balanced samples are selected using the Generalized Random Tessellation Stratified (GRTS) algorithm. The GRTS algorithm can be applied to finite resources (point geometries) and infinite resources (linear / linestring and areal / polygon geometries) and flexibly accommodates a diverse set of sampling design features, including stratification, unequal inclusion probabilities, proportional (to size) inclusion probabilities, legacy (historical) sites, a minimum distance between sites, and two options for replacement sites (reverse hierarchical order and nearest neighbor). Data are analyzed using a wide range of analysis functions that perform categorical variable analysis, continuous variable analysis, attributable risk analysis, risk difference analysis, relative risk analysis, change analysis, and trend analysis. spsurvey can also be used to summarize objects, visualize objects, select samples that are not spatially balanced, select panel samples, measure the amount of spatial balance in a sample, adjust design weights, and more. For additional details, see Dumelle et al. (2023) <doi:10.18637/jss.v105.i03>.

Maintained by Michael Dumelle. Last updated 7 months ago.

ord

12.4 match 15 stars 8.94 score 241 scripts 1 dependents

bioc

scDesign3:A unified framework of realistic in silico data generation and statistical model inference for single-cell and spatial omics

We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs, and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools.

Maintained by Dongyuan Song. Last updated 27 days ago.

software singlecell sequencing geneexpression spatial

14.6 match 89 stars 7.59 score 25 scripts

hypertidy

fasterize:Fast Polygon to Raster Conversion

Provides a drop-in replacement for rasterize() from the 'raster' package that takes polygon vector or data frame objects, and is much faster. There is support for the main options provided by the rasterize() function, including setting the field used and background value, and options for aggregating multi-layer rasters. Uses the scan line algorithm attributed to Wylie et al. (1967) <doi:10.1145/1465611.1465619>. Note that repository originally was hosted at 'Github' 'ecohealthalliance/fasterize' but was migrated to 'hypertidy/fasterize' in March 2025, and can be found indexed on 'R universe' <https://cran.r-universe.dev/fasterize>.

Maintained by Michael Sumner. Last updated 20 days ago.

raster rcpp rcpparmadillo sf spatial cpp

11.0 match 182 stars 10.05 score 14 dependents

bioc

spatialDE:R wrapper for SpatialDE

SpatialDE is a method to find spatially variable genes (SVG) from spatial transcriptomics data. This package provides wrappers to use the Python SpatialDE library in R, using reticulate and basilisk.

Maintained by Gabriele Sales. Last updated 5 months ago.

software transcriptomics python spatial-data wrapper

23.2 match 3 stars 4.76 score 16 scripts

bioc

simpleSeg:A package to perform simple cell segmentation

Image segmentation is the process of identifying the borders of individual objects (in this case cells) within an image. This allows for the features of cells such as marker expression and morphology to be extracted, stored and analysed. simpleSeg provides functionality for user friendly, watershed based segmentation on multiplexed cellular images in R based on the intensity of user specified protein marker channels. simpleSeg can also be used for the normalization of single cell data obtained from multiple images.

Maintained by Ellis Patrick. Last updated 5 months ago.

classification survival singlecell normalization spatial spatial-statistics

18.5 match 5.96 score 19 scripts 2 dependents

bioc

ggsc:Visualizing Single Cell and Spatial Transcriptomics

Useful functions to visualize single cell and spatial data. It supports visualizing 'Seurat', 'SingleCellExperiment' and 'SpatialExperiment' objects through grammar of graphics syntax implemented in 'ggplot2'.

Maintained by Guangchuang Yu. Last updated 5 months ago.

dimensionreduction geneexpression singlecell software spatial transcriptomics visualization openblas cpp openmp

14.5 match 47 stars 7.59 score 18 scripts

jeffreyevans

GeNetIt:Spatial Graph-Theoretic Genetic Gravity Modelling

Implementation of spatial graph-theoretic genetic gravity models. The model framework is applicable for other types of spatial flow questions. Includes functions for constructing spatial graphs, sampling and summarizing associated raster variables and building unconstrained and singly constrained gravity models.

Maintained by Jeffrey S. Evans. Last updated 2 years ago.

landscape-genetics r-spatial spatial statistics

25.7 match 9 stars 4.24 score 39 scripts

finleya

spBayes:Univariate and Multivariate Spatial-Temporal Modeling

Fits univariate and multivariate spatio-temporal random effects models for point-referenced data using Markov chain Monte Carlo (MCMC). Details are given in Finley, Banerjee, and Gelfand (2015) <doi:10.18637/jss.v063.i13> and Finley and Banerjee <doi:10.1016/j.envsoft.2019.104608>.

Maintained by Andrew Finley. Last updated 6 months ago.

openblas cpp openmp

23.7 match 1 stars 4.56 score 231 scripts 7 dependents

harryprince

geospark:Bring Local Sf to Spark

R binds 'GeoSpark' <http://geospark.datasyslab.org/> extending 'sparklyr' <https://spark.rstudio.com/> R package to make distributed 'geocomputing' easier. Sf is a package that provides [simple features] <https://en.wikipedia.org/wiki/Simple_Features> access for R and which is a leading 'geospatial' data processing tool. 'Geospark' R package bring the same simple features access like sf but running on Spark distributed system.

Maintained by Harry Zhu. Last updated 3 years ago.

apache-spark gis large-scale-spatial-analysis spark-sql sparklyr-extension spatial-analysis spatial-queries

21.3 match 57 stars 5.06 score 20 scripts

tidymodels

spatialsample:Spatial Resampling Infrastructure

Functions and classes for spatial resampling to use with the 'rsample' package, such as spatial cross-validation (Brenning, 2012) <doi:10.1109/IGARSS.2012.6352393>. The scope of 'rsample' and 'spatialsample' is to provide the basic building blocks for creating and analyzing resamples of a spatial data set, but neither package includes functions for modeling or computing statistics. The resampled spatial data sets created by 'spatialsample' do not contain much overhead in memory.

Maintained by Michael Mahoney. Last updated 6 months ago.

cpp

13.1 match 73 stars 8.19 score 118 scripts 2 dependents

r-forge

SpatialExtremes:Modelling Spatial Extremes

Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction. Other approaches (although not completely in agreement with the extreme value theory) are available such as the use of (spatial) copula and Bayesian hierarchical models assuming the so-called conditional assumptions. The latter approaches is handled through an (efficient) Gibbs sampler. Some key references: Davison et al. (2012) <doi:10.1214/11-STS376>, Padoan et al. (2010) <doi:10.1198/jasa.2009.tm08577>, Dombry et al. (2013) <doi:10.1093/biomet/ass067>.

Maintained by Mathieu Ribatet. Last updated 11 months ago.

openblas

19.9 match 5.36 score 189 scripts 2 dependents

bioc

OLIN:Optimized local intensity-dependent normalisation of two-color microarrays

Functions for normalisation of two-color microarrays by optimised local regression and for detection of artefacts in microarray data

Maintained by Matthias Futschik. Last updated 5 months ago.

microarray twochannel qualitycontrol preprocessing visualization

22.2 match 4.78 score 2 scripts 1 dependents

sjuhl

spfilteR:Semiparametric Spatial Filtering with Eigenvectors in (Generalized) Linear Models

Tools to decompose (transformed) spatial connectivity matrices and perform supervised or unsupervised semiparametric spatial filtering in a regression framework. The package supports unsupervised spatial filtering in standard linear as well as some generalized linear regression models.

Maintained by Sebastian Juhl. Last updated 3 days ago.

20.2 match 7 stars 5.24 score 10 scripts

stscl

cisp:A Correlation Indicator Based on Spatial Patterns

Use the spatial association marginal contributions derived from spatial stratified heterogeneity to capture the degree of correlation between spatial patterns.

Maintained by Wenbo Lv. Last updated 2 months ago.

association correlation geoinformatics spatial-patterns

20.7 match 5 stars 5.10 score 2 scripts

tmieno2

r.spatial.workshop.datasets:Collection of spatial datasets

This packages provides spatial datasets in various format. They are used for demonstrating spatial operations and map creation using R spatial pacakges (e.g., sf, terra, tmap).

Maintained by Taro Mieno. Last updated 6 months ago.

35.6 match 2.96 score 23 scripts

bioc

MoleculeExperiment:Prioritising a molecule-level storage of Spatial Transcriptomics Data

MoleculeExperiment contains functions to create and work with objects from the new MoleculeExperiment class. We introduce this class for analysing molecule-based spatial transcriptomics data (e.g., Xenium by 10X, Cosmx SMI by Nanostring, and Merscope by Vizgen). This allows researchers to analyse spatial transcriptomics data at the molecule level, and to have standardised data formats accross vendors.

Maintained by Shila Ghazanfar. Last updated 5 months ago.

dataimport datarepresentation infrastructure software spatial transcriptomics

16.3 match 12 stars 6.45 score 39 scripts

inlabru-org

inlabru:Bayesian Latent Gaussian Modelling using INLA and Extensions

Facilitates spatial and general latent Gaussian modeling using integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>). Additionally, extends the GAM-like model class to more general nonlinear predictor expressions, and implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. Model components are specified with general inputs and mapping methods to the latent variables, and the predictors are specified via general R expressions, with separate expressions for each observation likelihood model in multi-likelihood models. A prediction method based on fast Monte Carlo sampling allows posterior prediction of general expressions of the latent variables. Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) <doi:10.1111/2041-210X.13168>.

Maintained by Finn Lindgren. Last updated 3 days ago.

8.3 match 96 stars 12.60 score 832 scripts 6 dependents

ausgis

SecDim:The Second Dimension of Spatial Association

Most of the current methods explore spatial association using observations at sample locations, which are defined as the first dimension of spatial association (FDA). The proposed concept of the second dimension of spatial association (SDA), as described in Yongze Song (2022) <doi:10.1016/j.jag.2022.102834>, aims to extract in-depth information about the geographical environment from locations outside sample locations for exploring spatial association.

Maintained by Wenbo Lv. Last updated 7 months ago.

spatial-association spatial-predictions

38.6 match 1 stars 2.70 score 2 scripts

bioc

concordexR:Identify Spatial Homogeneous Regions with concordex

Spatial homogeneous regions (SHRs) in tissues are domains that are homogenous with respect to cell type composition. We present a method for identifying SHRs using spatial transcriptomics data, and demonstrate that it is efficient and effective at finding SHRs for a wide variety of tissue types. concordex relies on analysis of k-nearest-neighbor (kNN) graphs. The tool is also useful for analysis of non-spatial transcriptomics data, and can elucidate the extent of concordance between partitions of cells derived from clustering algorithms, and transcriptomic similarity as represented in kNN graphs.

Maintained by Kayla Jackson. Last updated 2 months ago.

singlecell clustering spatial transcriptomics

16.7 match 13 stars 6.23 score 13 scripts

ropensci

chopin:Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing

Geospatial data computation is parallelized by grid, hierarchy, or raster files. Based on future and mirai parallel backends, terra and sf functions as well as convenience functions in the package can be distributed over multiple threads. The simplest way of parallelizing generic geospatial computation is to start from `par_pad_*` functions to `par_grid`, `par_hierarchy`, or `par_multirasters` functions. Virtually any functions accepting classes in terra or sf packages can be used in the three parallelization functions. A common raster-vector overlay operation is provided as a function `extract_at`, which uses exactextractr, with options for kernel weights for summarizing raster values at vector geometries. Other convenience functions for vector-vector operations including simple areal interpolation (`summarize_aw`) and summation of exponentially decaying weights (`summarize_sedc`) are also provided.

Maintained by Insang Song. Last updated 27 days ago.

16.7 match 16 stars 6.11 score 23 scripts

bioc

visiumStitched:Enable downstream analysis of Visium capture areas stitched together with Fiji

This package provides helper functions for working with multiple Visium capture areas that overlap each other. This package was developed along with the companion example use case data available from https://github.com/LieberInstitute/visiumStitched_brain. visiumStitched prepares SpaceRanger (10x Genomics) output files so you can stitch the images from groups of capture areas together with Fiji. Then visiumStitched builds a SpatialExperiment object with the stitched data and makes an artificial hexogonal grid enabling the seamless use of spatial clustering methods that rely on such grid to identify neighboring spots, such as PRECAST and BayesSpace. The SpatialExperiment objects created by visiumStitched are compatible with spatialLIBD, which can be used to build interactive websites for stitched SpatialExperiment objects. visiumStitched also enables casting SpatialExperiment objects as Seurat objects.

Maintained by Nicholas J. Eagles. Last updated 4 months ago.

software spatial transcriptomics transcription geneexpression visualization dataimport 10xgenomics bioconductor spatial-transcriptomics spatialexperiment spatiallibd visium

19.0 match 1 stars 5.36 score 4 scripts

dieghernan

tidyterra:'tidyverse' Methods and 'ggplot2' Helpers for 'terra' Objects

Extension of the 'tidyverse' for 'SpatRaster' and 'SpatVector' objects of the 'terra' package. It includes also new 'geom_' functions that provide a convenient way of visualizing 'terra' objects with 'ggplot2'.

Maintained by Diego Hernangómez. Last updated 4 days ago.

terra ggplot-extension r-spatial rspatial

7.5 match 190 stars 13.59 score 1.9k scripts 25 dependents

bioc

GeomxTools:NanoString GeoMx Tools

Tools for NanoString Technologies GeoMx Technology. Package provides functions for reading in DCC and PKC files based on an ExpressionSet derived object. Normalization and QC functions are also included.

Maintained by Maddy Griswold. Last updated 5 months ago.

geneexpression transcription cellbasedassays dataimport transcriptomics proteomics mrnamicroarray proprietaryplatforms rnaseq sequencing experimentaldesign normalization spatial

14.2 match 7.11 score 239 scripts 3 dependents

ecospat

ecospat:Spatial Ecology Miscellaneous Methods

Collection of R functions and data sets for the support of spatial ecology analyses with a focus on pre, core and post modelling analyses of species distribution, niche quantification and community assembly. Written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) and Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland. Read Di Cola et al. (2016) <doi:10.1111/ecog.02671> for details.

Maintained by Olivier Broennimann. Last updated 2 months ago.

10.8 match 32 stars 9.35 score 418 scripts 1 dependents

jniedballa

camtrapR:Camera Trap Data Management and Preparation of Occupancy and Spatial Capture-Recapture Analyses

Management of and data extraction from camera trap data in wildlife studies. The package provides a workflow for storing and sorting camera trap photos (and videos), tabulates records of species and individuals, and creates detection/non-detection matrices for occupancy and spatial capture-recapture analyses with great flexibility. In addition, it can visualise species activity data and provides simple mapping functions with GIS export.

Maintained by Juergen Niedballa. Last updated 4 months ago.

occupancy-modeling spatial-capture-recapture wildlife

11.6 match 35 stars 8.65 score 178 scripts

matthewkling

phylospatial:Spatial Phylogenetic Analysis

Conduct various analyses on spatial phylogenetics. Use your data on an evolutionary tree and geographic distributions of the terminal taxa to compute diversity and endemism metrics, test significance with null model randomization, analyze community turnover and biotic regionalization, and perform spatial conservation prioritizations. All functions support quantitative community data in addition to binary data.

Maintained by Matthew Kling. Last updated 3 days ago.

16.1 match 6 stars 6.23 score 9 scripts