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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NEWS

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

Peer review:

Bug tracker:https://github.com/bafuentes/rassta/issues

On CRAN:

ecologygeoinformaticshierarchicalmodelingsamplingspatial

13 exports 16 stars 1.91 score 105 dependencies 19 scripts 1.1k downloads

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

TargetResultDate
Doc / VignettesOKAug 26 2024
R-4.5-winOKAug 26 2024
R-4.5-linuxOKAug 26 2024
R-4.4-winOKAug 26 2024
R-4.4-macOKAug 26 2024
R-4.3-winOKAug 26 2024
R-4.3-macOKAug 26 2024

Exports:dummiesenginefigurelocationsobservationplot3Dpredict_functionsselect_functionssignaturesimilaritysom_gapsom_pamstrata

Dependencies:askpassbackportsbase64encbitbit64broombroom.helpersbslibcachemclicliprclustercodetoolscolorspacecommonmarkcpp11crayoncrosstalkcurldata.tabledigestdplyrDTevaluatefansifarverfastmapfontawesomeforcatsforeachfsgenericsGGallyggplot2ggstatsgluegtablehavenhighrhistogramhmshtmltoolshtmlwidgetshttpuvhttrisobanditeratorsjquerylibjsonliteKernSmoothknitrkohonenlabelinglabelledlaterlatticelazyevallifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmeopensslpatchworkpillarpkgconfigplotlyplyrprettyunitsprogresspromisespurrrR6rappdirsRColorBrewerRcppreadrrlangrmarkdownsassscalesshinysourcetoolsstringdiststringistringrsysterratibbletidyrtidyselecttinytextzdbutf8vctrsviridisLitevroomwithrxfunxtableyaml

Classification Units

Rendered fromclassunits.Rmdusingknitr::rmarkdownon Aug 26 2024.

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

Landscape Similarity to Stratification Units

Rendered fromsimilarity.Rmdusingknitr::rmarkdownon Aug 26 2024.

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

Predictive Modeling Engine

Rendered frommodeling.Rmdusingknitr::rmarkdownon Aug 26 2024.

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

Spatial Signature of Classification Units

Rendered fromsignature.Rmdusingknitr::rmarkdownon Aug 26 2024.

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

Stratification Units

Rendered fromstratunits.Rmdusingknitr::rmarkdownon Aug 26 2024.

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

Stratified Non-Probability Sampling

Rendered fromsampling.Rmdusingknitr::rmarkdownon Aug 26 2024.

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