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:Bryan A. Fuentes [aut, cre], Minerva J. Dorantes [aut], John R. Tipton [aut], Robert J. Hijmans [ctb], Andrew G. Brown [ctb]

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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

On CRAN:

ecologygeoinformaticshierarchicalmodelingsamplingspatial

5.96 score 16 stars 19 scripts 331 downloads 13 exports 94 dependencies

Last updated 2 years agofrom:63068334c7. Checks:7 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 23 2025
R-4.5-winOKJan 23 2025
R-4.5-linuxOKJan 23 2025
R-4.4-winOKJan 23 2025
R-4.4-macOKJan 23 2025
R-4.3-winOKJan 23 2025
R-4.3-macOKJan 23 2025

Exports:dummiesenginefigurelocationsobservationplot3Dpredict_functionsselect_functionssignaturesimilaritysom_gapsom_pamstrata

Dependencies:askpassbase64encbslibcachemcliclustercodetoolscolorspacecommonmarkcpp11crayoncrosstalkcurldata.tabledigestdplyrDTevaluatefansifarverfastmapfontawesomeforcatsforeachfsgenericsGGallyggplot2ggstatsgluegtablehighrhistogramhmshtmltoolshtmlwidgetshttpuvhttrisobanditeratorsjquerylibjsonliteKernSmoothknitrkohonenlabelinglaterlatticelazyevallifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmeopensslpatchworkpillarpkgconfigplotlyplyrprettyunitsprogresspromisespurrrR6rappdirsRColorBrewerRcpprlangrmarkdownsassscalesshinysourcetoolsstringdiststringistringrsysterratibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunxtableyaml

Classification Units

Rendered fromclassunits.Rmdusingknitr::rmarkdownon Jan 23 2025.

Last update: 2021-12-09
Started: 2021-11-27

Landscape Similarity to Stratification Units

Rendered fromsimilarity.Rmdusingknitr::rmarkdownon Jan 23 2025.

Last update: 2021-11-27
Started: 2021-11-27

Predictive Modeling Engine

Rendered frommodeling.Rmdusingknitr::rmarkdownon Jan 23 2025.

Last update: 2022-07-25
Started: 2021-11-27

Spatial Signature of Classification Units

Rendered fromsignature.Rmdusingknitr::rmarkdownon Jan 23 2025.

Last update: 2021-11-27
Started: 2021-11-27

Stratification Units

Rendered fromstratunits.Rmdusingknitr::rmarkdownon Jan 23 2025.

Last update: 2021-11-27
Started: 2021-11-27

Stratified Non-Probability Sampling

Rendered fromsampling.Rmdusingknitr::rmarkdownon Jan 23 2025.

Last update: 2021-11-27
Started: 2021-11-27