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rassta

Raster-Based Spatial Stratification Algorithms

v1.0.6 · Aug 18, 2024 · AGPL (>= 3)

Description

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 et al. (2022). <doi:10.32614/RJ-2022-036>.

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Check History

OK 13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Jun 9, 2026
ERROR 12 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE Jun 8, 2026
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CRAN incoming feasibility

Maintainer: ‘Bryan A. Fuentes <bryandrep@gmail.com>’

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OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026

Dependency Network

Dependencies Reverse dependencies cluster data.table dplyr DT foreach GGally ggplot2 histogram KernSmooth (>= 2.23.18) kohonen plotly rlang (>= 0.4.11) scales shiny stringdist +2 more dependencies rassta

Version History

8 tracked
new 1.0.6 Mar 10, 2026
updated 1.0.6 ← 1.0.5 diff Aug 18, 2024
updated 1.0.5 ← 1.0.4 diff Aug 30, 2022
updated 1.0.4 ← 1.0.3 diff Jul 24, 2022
updated 1.0.3 ← 1.0.2 diff Feb 24, 2022
updated 1.0.2 ← 1.0.1 diff Dec 8, 2021
updated 1.0.1 ← 1.0.0 diff Oct 13, 2021
new 1.0.0 Sep 21, 2021