Package: spCF 0.2.2.1

spCF: Coarse-to-Fine Spatial and Spatio-Temporal Modeling

Provides functions for coarse-to-fine spatial and spatio-temporal modeling, enabling fast prediction, regression, and uncertainty quantification for moderate to large datasets. For methodological details, see Murakami et al. (2026) <doi:10.1111/gean.70034> and related work on generalized linear, downscaling, and dynamic spatio-temporal extensions.

Authors:Daisuke Murakami [aut, cre], Alexis Comber [aut], Takahiro Yoshida [aut], Narumasa Tsutsumida [aut], Chris Brunsdon [aut], Tomoki Nakaya [aut], Jose Luis Blanco-Claraco [ctb, cph], Marius Muja [cph], David G. Lowe [cph]

spCF_0.2.2.1.tar.gz
spCF_0.2.2.1.zip(r-4.7-x86_64)spCF_0.2.2.1.zip(r-4.7-arm64)spCF_0.2.2.1.zip(r-4.6-x86_64)spCF_0.2.2.1.zip(r-4.6-arm64)spCF_0.2.2.1.zip(r-4.5-x86_64)
spCF_0.2.2.1.tgz(r-4.6-x86_64)spCF_0.2.2.1.tgz(r-4.6-arm64)spCF_0.2.2.1.tgz(r-4.5-x86_64)spCF_0.2.2.1.tgz(r-4.5-arm64)
spCF_0.2.2.1.tar.gz(r-4.7-arm64)spCF_0.2.2.1.tar.gz(r-4.7-x86_64)spCF_0.2.2.1.tar.gz(r-4.6-arm64)spCF_0.2.2.1.tar.gz(r-4.6-x86_64)
spCF_0.2.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
spCF/json (API)

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

Bug tracker:https://github.com/dmuraka/spcf/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

5.30 score 2 stars 10 scripts 440 downloads 11 exports 14 dependencies

Last updated from:f171a5c185. Checks:15 OK. Indexed: yes.

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linux-devel-x86_64OK172
source / vignettesOK282
linux-release-arm64OK153
linux-release-x86_64OK194
macos-release-arm64OK148
macos-release-x86_64OK380
macos-oldrel-arm64OK179
macos-oldrel-x86_64OK328
windows-devel-arm64OK254
windows-devel-x86_64OK202
windows-release-arm64OK241
windows-release-x86_64OK171
windows-oldrel-x86_64OK180
wasm-releaseOK111

Exports:cf_dglmcf_dglm_hvcf_downscalecf_downscale_hvcf_glmcf_glm_hvcf_lmcf_lm_hvnegbinsp_scalewisespCFmap

Dependencies:dbscandotCall64fieldsFNNgenericslatticemapsMatrixnloptrRColorBrewerRcppspamviridisLitewithr

Coarse-to-fine dynamic space-time modeling: An application example
Data and setup | Coarse-to-fine dynamic spatial GLMM (CF-DGLM) | Space-time prediction | Predictive values at observed time points | Forecasting an unobserved time point | Prediction at new sites and times with predict() | Predictive quantiles and the size of the fitted object | Multiscale space-time pattern extraction | Interactive mapping | Reference
Rendered fromspCF_dglm.Rmdusingknitr::rmarkdown

Last update: 2026-10-07
Started: 2026-08-05

Coarse-to-fine spatial GLMM: Application examples
Example 1: Count data modeling for disease mapping | Data and setup | Coarse-to-fine spatial GLMM (CF-GLM) | Disease mapping | Predictive values | Multiscale spatial pattern/feature extraction | Example 2: Binary data modeling and prediction | Mapping | Prediction at new sites with predict() | Interactive mapping | Reference
Rendered fromspCF_glm.Rmdusingknitr::rmarkdown

Last update: 2026-10-07
Started: 2026-05-06

Coarse-to-fine spatial modeling (CFSM) using the spCF package
Data and setup | Coarse-to-fine spatial modeling | Mapping | Predictive values | Prediction at new sites with predict() | Multiscale spatial pattern/feature extraction | Interactive mapping | Reference
Rendered fromspCF_lm.Rmdusingknitr::rmarkdown

Last update: 2026-10-07
Started: 2026-08-05

Coarse-to-fine spatial downscaling (CF-DS): Application example
Data and setup | Coarse-to-fine spatial downscaling (CF-DS) | Mapping | Downscaling result | Predictive standard deviations | Reference
Rendered fromspCF_downscale.Rmdusingknitr::rmarkdown

Last update: 2026-08-05
Started: 2026-08-05