Package: spStack 1.1.3.99

spStack: Bayesian Geostatistics Using Predictive Stacking

Fits Bayesian hierarchical spatial and spatial-temporal 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 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 (2025) <doi:10.1080/01621459.2025.2566449>, and, Pan, Zhang, Bradley, and Banerjee (2025) <doi:10.1214/25-BA1582> for details.

Authors:Soumyakanti Pan [aut, cre], Sudipto Banerjee [aut]

spStack_1.1.3.99.tar.gz
spStack_1.1.3.99.zip(r-4.7-x86_64)spStack_1.1.3.99.zip(r-4.7-arm64)spStack_1.1.3.99.zip(r-4.6-x86_64)spStack_1.1.3.99.zip(r-4.6-arm64)spStack_1.1.3.99.zip(r-4.5-x86_64)
spStack_1.1.3.99.tgz(r-4.6-x86_64)spStack_1.1.3.99.tgz(r-4.6-arm64)spStack_1.1.3.99.tgz(r-4.5-x86_64)spStack_1.1.3.99.tgz(r-4.5-arm64)
spStack_1.1.3.99.tar.gz(r-4.7-arm64)spStack_1.1.3.99.tar.gz(r-4.7-x86_64)spStack_1.1.3.99.tar.gz(r-4.6-arm64)spStack_1.1.3.99.tar.gz(r-4.6-x86_64)
spStack_1.1.3.99.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
spStack/json (API)

# Install 'spStack' in R:
install.packages('spStack', repos = c('https://span-18.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/span-18/spstack-dev/issues

Pkgdown/docs site:https://span-18.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • simSpaceTime - Synthetic point-referenced spatial-temporal data
  • simSpatial - Synthetic point-referenced spatial data

On CRAN:

Conda:

openblascpp

5.57 score 1 stars 25 scripts 407 downloads 23 exports 40 dependencies

Last updated from:eaffc5c699. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK168
linux-devel-x86_64OK223
source / vignettesOK384
linux-release-arm64OK171
linux-release-x86_64OK192
macos-release-arm64OK183
macos-release-x86_64OK292
macos-oldrel-arm64OK145
macos-oldrel-x86_64OK527
windows-devel-arm64OK303
windows-devel-x86_64OK203
windows-release-arm64OK274
windows-release-x86_64OK204
windows-oldrel-x86_64OK192
wasm-releaseOK157

Exports:candidateModelscholUpdateDelcholUpdateDelBlockcholUpdateRankOneget_stacking_weightsiDistposteriorPredictrecoverGLMscalespGLMexactspGLMstackspLMexactspLMstackstackedSamplerstvcGLMexactstvcGLMstackstvcLMexactstvcLMstacksurfaceplotsurfaceplot2svcGLMexactsvcGLMstacksvcLMexactsvcLMstack

Dependencies:backportsBHcheckmateclarabelclicodetoolscpp11CVXRdigestfarverfuturefuture.applyggplot2globalsgluegmpgtablehighsisobandlabelinglatticelifecyclelistenvMatrixMBAosqpparallellyR6RColorBrewerRcppRcppEigenrlangrstudioapiS7scalesscsslamvctrsviridisLitewithr

Spatial-Temporal Regression Models
Data | Formula for varying coefficients model | Bayesian Gaussian spatially-temporally varying coefficient models | Bayesian non-Gaussian spatially-temporally varying coefficient models | Using fixed hyperparameters | Independent processes | Independent shared processes | Multivariate processes | Using predictive stacking
Rendered fromspatial-temporal.Rmdusingknitr::rmarkdown

Last update: 2026-10-12
Started: 2025-07-08

Spatial Regression Models
Bayesian Gaussian spatial regression models | Using fixed hyperparameters | Leave-one-out predictive densities using PSIS | Using predictive stacking | Analyzing samples from the stacked posterior | Analysis of spatial non-Gaussian data | Spatial Poisson count data | Under fixed hyperparameters | Posterior recovery of scale parameters | Sampling from stacked posterior | Spatial binomial count data | Spatial binary data | Spatially varying coefficients models | Poisson count data with spatially varying coefficients | References
Rendered fromspatial.Rmdusingknitr::rmarkdown

Last update: 2026-10-12
Started: 2025-07-08

Posterior Predictive Inference
Prediction in spatial linear model | Prediction in spatial generalized linear model | Prediction in spatially varying coefficients model | Prediction in spatially-temporally varying coefficients model
Rendered fromposterior-predictive.Rmdusingknitr::rmarkdown

Last update: 2026-10-12
Started: 2025-07-08

spStack: Bayesian Geostatistics Using Predictive Stacking
Bayesian Gaussian spatial regression models | Bayesian non-Gaussian spatial regression models | Additional functionalities | References
Rendered fromspStack.Rmdusingknitr::rmarkdown

Last update: 2026-10-12
Started: 2024-09-29

Technical Overview
Introduction | Bayesian Gaussian spatial regression models | Bayesian non-Gaussian spatial regression models | Bayesian non-Gaussian spatial-temporal regression model | Predictive stacking | References
Rendered fromtechnical_overview.Rmdusingknitr::rmarkdown

Last update: 2025-07-11
Started: 2025-07-08

Readme and manuals

Help Manual

Help pageTopics
spStack: Bayesian Geostatistics Using Predictive StackingspStack-package spStack
Create a collection of candidate models for stackingcandidateModels
Different Cholesky factor updatescholUpdate cholUpdateDel cholUpdateDelBlock cholUpdateRankOne
Optimal stacking weightsget_stacking_weights
Calculate distance matrixiDist
Prediction of latent process at new spatial or temporal locationsposteriorPredict
Recover posterior samples of scale parameters of spatial/spatial-temporal generalized linear modelsrecoverGLMscale
Synthetic point-referenced spatial-temporal datasimSpaceTime
Synthetic point-referenced spatial datasimSpatial
Univariate Bayesian spatial generalized linear modelspGLMexact
Bayesian spatial generalized linear model using predictive stackingspGLMstack
Univariate Bayesian spatial linear modelspLMexact
Bayesian spatial linear model using predictive stackingspLMstack
Sample from the stacked posterior distributionstackedSampler
Bayesian spatially-temporally varying generalized linear modelstvcGLMexact
Bayesian spatially-temporally varying coefficients generalized linear model using predictive stackingstvcGLMstack
Bayesian spatially-temporally varying coefficients linear modelstvcLMexact
Bayesian spatially-temporally varying coefficients linear model using predictive stackingstvcLMstack
Make a surface plotsurfaceplot
Make two side-by-side surface plotssurfaceplot2
Bayesian spatially varying coefficients generalized linear modelsvcGLMexact
Bayesian spatially varying coefficients generalized linear model using predictive stackingsvcGLMstack
Bayesian spatially varying coefficients linear modelsvcLMexact
Bayesian spatially varying coefficients linear model using predictive stackingsvcLMstack