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]

rassta_1.0.5.tar.gz
rassta_1.0.5.zip(r-4.7-any)rassta_1.0.5.zip(r-4.6-any)rassta_1.0.5.zip(r-4.5-any)
rassta_1.0.5.tgz(r-4.6-any)rassta_1.0.5.tgz(r-4.5-any)
rassta_1.0.5.tar.gz(r-4.7-any)rassta_1.0.5.tar.gz(r-4.6-any)
rassta_1.0.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
rassta/json (API)

# 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/docs site:https://bafuentes.github.io

On CRAN:

Conda:

ecologygeoinformaticshierarchicalmodelingsamplingspatial

6.19 score 16 stars 32 scripts 363 downloads 13 exports 87 dependencies

Last updated from:63068334c7. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK153
source / vignettesOK300
linux-releaseOK204
macos-releaseOK208
macos-oldrelOK171
windows-develOK147
windows-releaseOK119
windows-oldrelOK119
wasm-releaseOK139

Exports:dummiesenginefigurelocationsobservationplot3Dpredict_functionsselect_functionssignaturesimilaritysom_gapsom_pamstrata

Dependencies:askpassbase64encbslibcachemcliclustercodetoolscommonmarkcpp11crayoncrosstalkcurldata.tabledigestdplyrDTevaluatefarverfastmapfontawesomeforcatsforeachfsgenericsGGallyggplot2ggstatsgluegtablehighrhistogramhmshtmltoolshtmlwidgetshttpuvhttrisobanditeratorsjquerylibjsonliteKernSmoothknitrkohonenlabelinglaterlazyevallifecyclemagrittrmemoisemimeopensslotelpatchworkpillarpkgconfigplotlyprettyunitsprogresspromisespurrrR6rappdirsRColorBrewerRcpprlangrmarkdownS7sassscalesshinysourcetoolsstringdiststringistringrsysterratibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunxtableyaml

Predictive Modeling Engine
References
Rendered frommodeling.Rmdusingknitr::rmarkdown

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

Classification Units
Dimension reduction and estimation of k | Rasterization of the SOM and PAM outputs | Reclassification | References
Rendered fromclassunits.Rmdusingknitr::rmarkdown

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

Landscape Similarity to Stratification Units
References
Rendered fromsimilarity.Rmdusingknitr::rmarkdown

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

Spatial Signature of Classification Units
Selection of Distribution Functions | Estimation and prediction of distribution functions | Aggregation of predicted distribution functions | References
Rendered fromsignature.Rmdusingknitr::rmarkdown

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

Stratification Units
Spatial intersection of classification units | Numeric code of stratification units | References
Rendered fromstratunits.Rmdusingknitr::rmarkdown

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

Stratified Non-Probability Sampling
Selection of representative observations | Selection of representative sampling locations | References
Rendered fromsampling.Rmdusingknitr::rmarkdown

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