Showing 42 of total 42 results (show query)
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.
26.4 match 4.36 score 46 scripts
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 10 days ago.
7.7 match 197 stars 14.78 score 4.8k scripts 57 dependents
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.
12.8 match 10 stars 7.57 score 1.8k scripts 12 dependents
rubenfcasal
npsp:Nonparametric Spatial Statistics
Multidimensional nonparametric spatial (spatio-temporal) geostatistics. S3 classes and methods for multidimensional: linear binning, local polynomial kernel regression (spatial trend estimation), density and variogram estimation. Nonparametric methods for simultaneous inference on both spatial trend and variogram functions (for spatial processes). Nonparametric residual kriging (spatial prediction). For details on these methods see, for example, Fernandez-Casal and Francisco-Fernandez (2014) <doi:10.1007/s00477-013-0817-8> or Castillo-Paez et al. (2019) <doi:10.1016/j.csda.2019.01.017>.
Maintained by Ruben Fernandez-Casal. Last updated 4 months ago.
geostatisticsspatial-data-analysisstatisticsfortranopenblas
16.0 match 4 stars 5.71 score 64 scripts
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 10 days ago.
7.7 match 4.95 score 6 scripts
jfrench
gear:Geostatistical Analysis in R
Implements common geostatistical methods in a clean, straightforward, efficient manner. The methods are discussed in Schabenberger and Gotway (2004, <ISBN:9781584883227>) and Waller and Gotway (2004, <ISBN:9780471387718>).
Maintained by Joshua French. Last updated 5 years ago.
21.9 match 1.43 score 27 scripts
eborgnine
geostatsp:Geostatistical Modelling with Likelihood and Bayes
Geostatistical modelling facilities using 'SpatRaster' and 'SpatVector' objects are provided. Non-Gaussian models are fit using 'INLA', and Gaussian geostatistical models use Maximum Likelihood Estimation. For details see Brown (2015) <doi:10.18637/jss.v063.i12>. The 'RandomFields' package is available at <https://www.wim.uni-mannheim.de/schlather/publications/software>.
Maintained by Patrick Brown. Last updated 1 months ago.
7.8 match 5 stars 3.94 score 73 scripts
cran
gmGeostats:Geostatistics for Compositional Analysis
Support for geostatistical analysis of multivariate data, in particular data with restrictions, e.g. positive amounts, compositions, distributional data, microstructural data, etc. It includes descriptive analysis and modelling for such data, both from a two-point Gaussian perspective and multipoint perspective. The methods mainly follow Tolosana-Delgado, Mueller and van den Boogaart (2018) <doi:10.1007/s11004-018-9769-3>.
Maintained by K. Gerald van den Boogaart. Last updated 2 years ago.
8.3 match 1 stars 3.00 score
hanzifei
gcKrig:Analysis of Geostatistical Count Data using Gaussian Copulas
Provides a variety of functions to analyze and model geostatistical count data with Gaussian copulas, including 1) data simulation and visualization; 2) correlation structure assessment (here also known as the Normal To Anything); 3) calculate multivariate normal rectangle probabilities; 4) likelihood inference and parallel prediction at predictive locations. Description of the method is available from: Han and DeOliveira (2018) <doi:10.18637/jss.v087.i13>.
Maintained by Zifei Han. Last updated 3 years ago.
20.4 match 1 stars 1.15 score 14 scripts
amsantac
geospt:Geostatistical Analysis and Design of Optimal Spatial Sampling Networks
Estimation of the variogram through trimmed mean, radial basis functions (optimization, prediction and cross-validation), summary statistics from cross-validation, pocket plot, and design of optimal sampling networks through sequential and simultaneous points methods.
Maintained by Ali Santacruz. Last updated 6 days ago.
4.6 match 4 stars 4.56 score 30 scripts 1 dependents
deankoch
snapKrig:Fast Kriging and Geostatistics on Grids with Kronecker Covariance
Geostatistical modeling and kriging with gridded data using spatially separable covariance functions (Kronecker covariances). Kronecker products in these models provide shortcuts for solving large matrix problems in likelihood and conditional mean, making 'snapKrig' computationally efficient with large grids. The package supplies its own S3 grid object class, and a host of methods including plot, print, Ops, square bracket replace/assign, and more. Our computational methods are described in Koch, Lele, Lewis (2020) <doi:10.7939/r3-g6qb-bq70>.
Maintained by Dean Koch. Last updated 5 months ago.
3.4 match 5 stars 5.05 score 15 scriptscran
sgeostat:An Object-Oriented Framework for Geostatistical Modeling in S+
An Object-oriented Framework for Geostatistical Modeling in S+ containing functions for variogram estimation, variogram fitting and kriging as well as some plot functions. Written entirely in S, therefore works only for small data sets in acceptable computing time.
Maintained by Albrecht Gebhardt. Last updated 9 years ago.
3.4 match 1 stars 3.42 score 23 dependents
laboratorio-de-pedometria
pedometrics:Miscellaneous Pedometric Tools
An R implementation of methods employed in the field of pedometrics, soil science discipline dedicated to studying the spatial, temporal, and spatio-temporal variation of soil using statistical and computational methods. The methods found here include the calibration of linear regression models using covariate selection strategies, computation of summary validation statistics for predictions, generation of summary plots, evaluation of the local quality of a geostatistical model of uncertainty, and so on. Other functions simply extend the functionalities of or facilitate the usage of functions from other packages that are commonly used for the analysis of soil data. Formerly available versions of suggested packages no longer available from CRAN can be obtained from the CRAN archive <https://cran.r-project.org/src/contrib/Archive/>.
Maintained by Alessandro Samuel-Rosa. Last updated 3 years ago.
2.4 match 6 stars 4.00 score 33 scripts
lrcastro
spANOVA:Analysis of Field Trials with Geostatistics & Spatial AR Models
Perform analysis of variance when the experimental units are spatially correlated. There are two methods to deal with spatial dependence: Spatial autoregressive models (see Rossoni, D. F., & Lima, R. R. (2019) <doi:10.28951/rbb.v37i2.388>) and geostatistics (see Pontes, J. M., & Oliveira, M. S. D. (2004) <doi:10.1590/S1413-70542004000100018>). For both methods, there are three multicomparison procedure available: Tukey, multivariate T, and Scott-Knott.
Maintained by Castro L. R.. Last updated 12 months ago.
5.1 match 1.70 score 2 scripts
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 7 months ago.
1.8 match 4 stars 4.90 score 10 scripts
thomasasmith
CRTspat:Workflow for Cluster Randomised Trials with Spillover
Design, workflow and statistical analysis of Cluster Randomised Trials of (health) interventions where there may be spillover between the arms (see <https://thomasasmith.github.io/index.html>).
Maintained by Thomas Smith. Last updated 21 days ago.
1.3 match 4 stars 6.54 score 24 scripts
strevisani
SurfRough:Calculate Surface/Image Texture Indexes
Methods for the computation of surface/image texture indices using a geostatistical based approach (Trevisani et al. (2023) <doi:10.1016/j.geomorph.2023.108838>). It provides various functions for the computation of surface texture indices (e.g., omnidirectional roughness and roughness anisotropy), including the ones based on the robust MAD estimator. The kernels included in the software permit also to calculate the surface/image texture indices directly from the input surface (i.e., without de-trending) using increments of order 2. It also provides the new radial roughness index (RRI), representing the improvement of the popular topographic roughness index (TRI). The framework can be easily extended with ad-hoc surface/image texture indices.
Maintained by Sebastiano Trevisani. Last updated 1 days ago.
2.2 match 1 stars 3.65 score
adrian-bowman
rpanel:Simple Interactive Controls for R using the 'tcltk' Package
A set of functions to build simple GUI controls for R functions. These are built on the 'tcltk' package. Uses could include changing a parameter on a graph by animating it with a slider or a "doublebutton", up to more sophisticated control panels. Some functions for specific graphical tasks, referred to as 'cartoons', are provided.
Maintained by Adrian Bowman. Last updated 2 years ago.
1.8 match 1 stars 4.30 score 157 scripts 9 dependents
brian-j-smith
ramps:Bayesian Geostatistical Modeling with RAMPS
Bayesian geostatistical modeling of Gaussian processes using a reparameterized and marginalized posterior sampling (RAMPS) algorithm designed to lower autocorrelation in MCMC samples. Package performance is tuned for large spatial datasets.
Maintained by Brian J Smith. Last updated 2 years ago.
5.4 match 1.41 score 26 scripts
jskoien
rtop:Interpolation of Data with Variable Spatial Support
Data with irregular spatial support, such as runoff related data or data from administrative units, can with 'rtop' be interpolated to locations without observations with the top-kriging method. A description of the package is given by Skøien et al (2014) <doi:10.1016/j.cageo.2014.02.009>.
Maintained by Jon Olav Skøien. Last updated 1 years ago.
1.9 match 5 stars 2.83 score 45 scripts 1 dependents
gilberto-sassi
geoFourierFDA:Ordinary Functional Kriging Using Fourier Smoothing and Gaussian Quadrature
Implementation of the ordinary functional kriging method proposed by Giraldo (2011) <doi:10.1007/s10651-010-0143-y>. This implements an alternative method to estimate the trace-variogram using Fourier Smoothing and Gaussian Quadrature.
Maintained by Gilberto Sassi. Last updated 4 years ago.
1.8 match 2.70 score 3 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.
0.8 match 3 stars 5.46 score 107 scripts 3 dependentscran
geotoolsR:Tools to Improve the Use of Geostatistic
The basic idea of this package is provides some tools to help the researcher to work with geostatistics. Initially, we present a collection of functions that allow the researchers to deal with spatial data using bootstrap procedure. There are five methods available and two ways to display them: bootstrap confidence interval - provides a two-sided bootstrap confidence interval; bootstrap plot - a graphic with the original variogram and each of the B bootstrap variograms.
Maintained by Diogo Francisco Rossoni. Last updated 8 months ago.
3.6 match 1.00 score 5 scripts
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.
0.5 match 5 stars 6.44 score 328 scripts 1 dependentscran
geoBayes:Analysis of Geostatistical Data using Bayes and Empirical Bayes Methods
Functions to fit geostatistical data. The data can be continuous, binary or count data and the models implemented are flexible. Conjugate priors are assumed on some parameters while inference on the other parameters can be done through a full Bayesian analysis of by empirical Bayes methods.
Maintained by Evangelos Evangelou. Last updated 5 months ago.
3.3 match 1.00 score
drsimonspencer
AMISforInfectiousDiseases:Implement the AMIS Algorithm for Infectious Disease Models
Implements the Adaptive Multiple Importance Sampling (AMIS) algorithm, as described by Retkute et al. (2021, <doi:10.1214/21-AOAS1486>), to estimate key epidemiological parameters by combining outputs from a geostatistical model of infectious diseases (such as prevalence, incidence, or relative risk) with a disease transmission model. Utilising the resulting posterior distributions, the package enables forward projections at the local level.
Maintained by Simon Spencer. Last updated 2 months ago.
0.5 match 5.82 score 6 scripts
aleksandarsekulic
meteo:RFSI & STRK Interpolation for Meteo and Environmental Variables
Random Forest Spatial Interpolation (RFSI, Sekulić et al. (2020) <doi:10.3390/rs12101687>) and spatio-temporal geostatistical (spatio-temporal regression Kriging (STRK)) interpolation for meteorological (Kilibarda et al. (2014) <doi:10.1002/2013JD020803>, Sekulić et al. (2020) <doi:10.1007/s00704-019-03077-3>) and other environmental variables. Contains global spatio-temporal models calculated using publicly available data.
Maintained by Aleksandar Sekulić. Last updated 6 months ago.
0.5 match 18 stars 5.06 score 64 scripts
f-rousset
spaMM:Mixed-Effect Models, with or without Spatial Random Effects
Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the 'INLA' package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.
Maintained by François Rousset. Last updated 9 months ago.
0.5 match 4.94 score 208 scripts 5 dependentsjskoien
intamapInteractive:Interactive Add-on Functionality for 'intamap'
The methods in this package adds to the functionality of the 'intamap' package, such as bias correction and network optimization. Pebesma et al (2010) gives an overview of the methods behind and possible usage <doi:10.1016/j.cageo.2010.03.019>.
Maintained by Jon Skoien. Last updated 1 years ago.
1.6 match 1.41 score 26 scripts
lucapresicce
spBPS:Bayesian Predictive Stacking for Scalable Geospatial Transfer Learning
Provides functions for Bayesian Predictive Stacking within the Bayesian transfer learning framework for geospatial artificial systems, as introduced in "Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach" (Presicce and Banerjee, 2024) <doi:10.48550/arXiv.2410.09504>. This methodology enables efficient Bayesian geostatistical modeling, utilizing predictive stacking to improve inference across spatial datasets. The core functions leverage 'C++' for high-performance computation, making the framework well-suited for large-scale spatial data analysis in parallel and distributed computing environments. Designed for scalability, it allows seamless application in computationally demanding scenarios.
Maintained by Luca Presicce. Last updated 5 months ago.
0.5 match 4.40 score 10 scripts
hakyimlab
OmicKriging:Poly-Omic Prediction of Complex TRaits
It provides functions to generate a correlation matrix from a genetic dataset and to use this matrix to predict the phenotype of an individual by using the phenotypes of the remaining individuals through kriging. Kriging is a geostatistical method for optimal prediction or best unbiased linear prediction. It consists of predicting the value of a variable at an unobserved location as a weighted sum of the variable at observed locations. Intuitively, it works as a reverse linear regression: instead of computing correlation (univariate regression coefficients are simply scaled correlation) between a dependent variable Y and independent variables X, it uses known correlation between X and Y to predict Y.
Maintained by Hae Kyung Im. Last updated 4 years ago.
0.5 match 2 stars 4.38 score 48 scripts
jmhewitt
telefit:Estimation and Prediction for Remote Effects Spatial Process Models
Implementation of the remote effects spatial process (RESP) model for teleconnection. The RESP model is a geostatistical model that allows a spatially-referenced variable (like average precipitation) to be influenced by covariates defined on a remote domain (like sea surface temperatures). The RESP model is introduced in Hewitt et al. (2018) <doi:10.1002/env.2523>. Sample code for working with the RESP model is available at <https://jmhewitt.github.io/research/resp_example>. This material is based upon work supported by the National Science Foundation under grant number AGS 1419558. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Maintained by Joshua Hewitt. Last updated 5 years ago.
0.5 match 1 stars 3.19 score 31 scriptsjskoien
intamap:Procedures for Automated Interpolation
Geostatistical interpolation has traditionally been done by manually fitting a variogram and then interpolating. Here, we introduce classes and methods that can do this interpolation automatically. Pebesma et al (2010) gives an overview of the methods behind and possible usage <doi:10.1016/j.cageo.2010.03.019>.
Maintained by Jon Olav Skoien. Last updated 1 years ago.
0.5 match 1 stars 2.61 score 68 scripts 2 dependentscran
GeoAdjust:Accounting for Random Displacements of True GPS Coordinates of Data
The purpose is to account for the random displacements (jittering) of true survey household cluster center coordinates in geostatistical analyses of Demographic and Health Surveys program (DHS) data. Adjustment for jittering can be implemented either in the spatial random effect, or in the raster/distance based covariates, or in both. Detailed information about the methods behind the package functionality can be found in two preprints. Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2022) <arXiv:2202.11035v2>. Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2022) <arXiv:2211.07442v1>.
Maintained by Umut Altay. Last updated 1 years ago.
0.5 match 1.70 score 1 scripts