Package: rassta 1.0.5
rassta: Raster-Based Spatial Stratification Algorithms
Algorithms for the spatial stratification of landscapes, sampling and modeling of spatially-varying phenomena. These algorithms offer a simple framework for the stratification of geographic space based on raster layers representing landscape factors and/or factor scales. The stratification process follows a hierarchical approach, which is based on first level units (i.e., classification units) and second-level units (i.e., stratification units). Nonparametric techniques allow to measure the correspondence between the geographic space and the landscape configuration represented by the units. These correspondence metrics are useful to define sampling schemes and to model the spatial variability of environmental phenomena. The theoretical background of the algorithms and code examples are presented in Fuentes, Dorantes, and Tipton (2021). <doi:10.31223/X50S57>.
Authors:
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rassta.pdf |rassta.html✨
rassta/json (API)
NEWS
# Install 'rassta' in R: |
install.packages('rassta', repos = c('https://bafuentes.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/bafuentes/rassta/issues
Pkgdown site:https://bafuentes.github.io
ecologygeoinformaticshierarchicalmodelingsamplingspatial
Last updated 2 years agofrom:63068334c7. Checks:7 OK. Indexed: yes.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Jan 23 2025 |
R-4.5-win | OK | Jan 23 2025 |
R-4.5-linux | OK | Jan 23 2025 |
R-4.4-win | OK | Jan 23 2025 |
R-4.4-mac | OK | Jan 23 2025 |
R-4.3-win | OK | Jan 23 2025 |
R-4.3-mac | OK | Jan 23 2025 |
Exports:dummiesenginefigurelocationsobservationplot3Dpredict_functionsselect_functionssignaturesimilaritysom_gapsom_pamstrata
Dependencies:askpassbase64encbslibcachemcliclustercodetoolscolorspacecommonmarkcpp11crayoncrosstalkcurldata.tabledigestdplyrDTevaluatefansifarverfastmapfontawesomeforcatsforeachfsgenericsGGallyggplot2ggstatsgluegtablehighrhistogramhmshtmltoolshtmlwidgetshttpuvhttrisobanditeratorsjquerylibjsonliteKernSmoothknitrkohonenlabelinglaterlatticelazyevallifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmeopensslpatchworkpillarpkgconfigplotlyplyrprettyunitsprogresspromisespurrrR6rappdirsRColorBrewerRcpprlangrmarkdownsassscalesshinysourcetoolsstringdiststringistringrsysterratibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunxtableyaml
Classification Units
Rendered fromclassunits.Rmd
usingknitr::rmarkdown
on Jan 23 2025.Last update: 2021-12-09
Started: 2021-11-27
Landscape Similarity to Stratification Units
Rendered fromsimilarity.Rmd
usingknitr::rmarkdown
on Jan 23 2025.Last update: 2021-11-27
Started: 2021-11-27
Predictive Modeling Engine
Rendered frommodeling.Rmd
usingknitr::rmarkdown
on Jan 23 2025.Last update: 2022-07-25
Started: 2021-11-27
Spatial Signature of Classification Units
Rendered fromsignature.Rmd
usingknitr::rmarkdown
on Jan 23 2025.Last update: 2021-11-27
Started: 2021-11-27
Stratification Units
Rendered fromstratunits.Rmd
usingknitr::rmarkdown
on Jan 23 2025.Last update: 2021-11-27
Started: 2021-11-27
Stratified Non-Probability Sampling
Rendered fromsampling.Rmd
usingknitr::rmarkdown
on Jan 23 2025.Last update: 2021-11-27
Started: 2021-11-27