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)spStack_1.1.3.99.zip(r-4.6)spStack_1.1.3.99.zip(r-4.5)
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✨
card.svg |card.png
spStack/json (API)
NEWS

# 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:
  • sim_stvcPoisson - Synthetic point-referenced spatial-temporal Poisson count data simulated using spatially-temporally varying coefficients
  • simBinary - Synthetic point-referenced binary data
  • simBinom - Synthetic point-referenced binomial count data
  • simGaussian - Synthetic point-referenced Gaussian data
  • simPoisson - Synthetic point-referenced Poisson count data

On CRAN:

Conda:

openblascpp

5.54 score 1 stars 20 scripts 572 downloads 18 exports 53 dependencies

Last updated from:9f8d20e484. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK209
linux-devel-x86_64OK232
source / vignettesOK334
linux-release-arm64OK214
linux-release-x86_64OK203
macos-release-arm64OK204
macos-release-x86_64OK306
macos-oldrel-arm64OK195
macos-oldrel-x86_64OK530
windows-develOK171
windows-releaseOK171
windows-oldrelOK160
wasm-releaseOK167

Exports:candidateModelscholUpdateDelcholUpdateDelBlockcholUpdateRankOneget_stacking_weightsiDistposteriorPredictrecoverGLMscalesim_spDataspGLMexactspGLMstackspLMexactspLMstackstackedSamplerstvcGLMexactstvcGLMstacksurfaceplotsurfaceplot2

Dependencies:abindbackportsBHcheckmateclarabelclicodetoolscpp11CVXRdigestdistributionalfarverfuturefuture.applygenericsggplot2globalsgluegmpgtablehighsisobandlabelinglatticelifecyclelistenvloomagrittrMatrixmatrixStatsMBAnumDerivosqpparallellypillarpkgconfigposteriorR6RColorBrewerRcppRcppEigenrlangrstudioapiS7scalesscsslamtensorAtibbleutf8vctrsviridisLitewithr

Posterior Predictive Inference

Rendered fromposterior-predictive.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2026-05-18
Started: 2025-07-08

Spatial Regression Models

Rendered fromspatial.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2026-05-18
Started: 2025-07-08

Spatial-Temporal Regression Models

Rendered fromspatial-temporal.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2026-03-08
Started: 2025-07-08

spStack: Bayesian Geostatistics Using Predictive Stacking

Rendered fromspStack.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2026-05-18
Started: 2024-09-29

Technical Overview

Rendered fromtechnical_overview.Rmdusingknitr::rmarkdownon May 18 2026.

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
Simulate spatial data on unit squaresim_spData
Synthetic point-referenced spatial-temporal Poisson count data simulated using spatially-temporally varying coefficientssim_stvcPoisson
Synthetic point-referenced binary datasimBinary
Synthetic point-referenced binomial count datasimBinom
Synthetic point-referenced Gaussian datasimGaussian
Synthetic point-referenced Poisson count datasimPoisson
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
Make a surface plotsurfaceplot
Make two side-by-side surface plotssurfaceplot2