Package: stcvlite 0.3.1.9000

stcvlite: Lightweight rsample extension for spatiotemporal cross-validation

The main functions provide rset generators for spatiotemporal cross-validation with low footprint.

Authors:Insang Song [aut, cre]

stcvlite_0.3.1.9000.tar.gz
stcvlite_0.3.1.9000.zip(r-4.7-any)stcvlite_0.3.1.9000.zip(r-4.6-any)stcvlite_0.3.1.9000.zip(r-4.5-any)
stcvlite_0.3.1.9000.tgz(r-4.6-any)stcvlite_0.3.1.9000.tgz(r-4.5-any)
stcvlite_0.3.1.9000.tar.gz(r-4.7-any)stcvlite_0.3.1.9000.tar.gz(r-4.6-any)
stcvlite_0.3.1.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
stcvlite/json (API)

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

Bug tracker:https://github.com/sigmafelix/stcvlite/issues

Pkgdown/docs site:https://sigmafelix.github.io

Datasets:
  • spdat - Sample full spatiotemporal point data

On CRAN:

Conda:

3.48 score 1 stars 4 scripts 13 exports 45 dependencies

Last updated from:f7b981c25b. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develNOTE224
source / vignettesOK505
linux-releaseNOTE223
macos-releaseNOTE177
macos-oldrelNOTE133
windows-develNOTE180
windows-releaseNOTE159
windows-oldrelNOTE175
wasm-releaseOK144

Exports:convert_cv_index_rsetgenerate_block_sp_indexgenerate_cv_indexgenerate_cv_index_lblogenerate_cv_index_lbltogenerate_cv_index_lbtogenerate_cv_index_lologenerate_cv_index_loltogenerate_cv_index_lotogenerate_cv_index_randomgenerate_spt_indexplot_cv_foldsprep_input

Dependencies:classclassIntclicodetoolscpp11data.tableDBIdbscandigestdplyre1071FNNfurrrfuturegenericsglobalsglueKernSmoothlifecyclelistenvmagrittrparallellypillarpkgconfigproxypurrrR6Rcpprlangrsamples2sfsliderstringistringrterratibbletidyrtidyselectunitsutf8vctrswarpwithrwk

Spatiotemporal CV on Air Quality Data with torch Neural Networks
Note | Introduction | 1. Data and features | 2. Cross-validation indices | 3. Scaling within each fold | 4. The network | 5. Training and prediction helper | 6. Running the four schemes | 7. Reading a training curve | MLP takeaways | 8. From independent rows to a graph | 9. Building the station graph | 10. From rows to a station-day panel | 11. A GraphSAGE-style model | 12. Training across folds | 13. MLP vs. GCN, side by side | Overall takeaways
Rendered fromair-torch-deeplearning.Rmdusingknitr::rmarkdown

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

Spatiotemporal CV on gstat's Air Quality Data with xgboost and BART
Introduction | 1. The data: gstat's rural-background PM10 stations | Station coordinates | 2. Preparing the covariate table | 3. Spatiotemporal indices and station-coordinate blocks | 4. Every cross-validation scheme, side by side | 5. Prediction models: xgboost and BART | 6. Comparing the schemes | Takeaways
Rendered fromair-xgboost-bart.Rmdusingknitr::rmarkdown

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