The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

sgsR: Structurally Guided Sampling

Structurally guided sampling (SGS) approaches for airborne laser scanning (ALS; LIDAR). Primary functions provide means to generate data-driven stratifications & methods for allocating samples. Intermediate functions for calculating and extracting important information about input covariates and samples are also included. Processing outcomes are intended to help forest and environmental management practitioners better optimize field sample placement as well as assess and augment existing sample networks in the context of data distributions and conditions. ALS data is the primary intended use case, however any rasterized remote sensing data can be used, enabling data-driven stratifications and sampling approaches.

Version: 1.4.5
Depends: R (≥ 3.5.0), methods
Imports: dplyr, ggplot2, sf, terra, tidyr, clhs, SamplingBigData, BalancedSampling, spatstat.geom
Suggests: knitr, rmarkdown, Rfast, testthat (≥ 3.0.0), doParallel, doSNOW, snow, foreach, entropy, roxygen2, covr, RANN, spelling
Published: 2024-03-03
DOI: 10.32614/CRAN.package.sgsR
Author: Tristan RH Goodbody ORCID iD [aut, cre, cph], Nicholas C Coops ORCID iD [aut], Martin Queinnec ORCID iD [aut]
Maintainer: Tristan RH Goodbody <goodbody.t at gmail.com>
BugReports: https://github.com/tgoodbody/sgsR/issues
License: GPL (≥ 3)
URL: https://github.com/tgoodbody/sgsR, https://tgoodbody.github.io/sgsR/
NeedsCompilation: no
Language: en-US
Citation: sgsR citation info
Materials: README NEWS
CRAN checks: sgsR results

Documentation:

Reference manual: sgsR.pdf
Vignettes: Calculating
Sampling
sgsR
Stratification

Downloads:

Package source: sgsR_1.4.5.tar.gz
Windows binaries: r-devel: sgsR_1.4.5.zip, r-release: sgsR_1.4.5.zip, r-oldrel: sgsR_1.4.5.zip
macOS binaries: r-release (arm64): sgsR_1.4.5.tgz, r-oldrel (arm64): sgsR_1.4.5.tgz, r-release (x86_64): sgsR_1.4.5.tgz, r-oldrel (x86_64): sgsR_1.4.5.tgz
Old sources: sgsR archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=sgsR to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.