Package: sgsR 1.5.0

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.

Authors:Tristan RH Goodbody [aut, cre, cph], Nicholas C Coops [aut], Martin Queinnec [aut]

sgsR_1.5.0.tar.gz
sgsR_1.5.0.zip(r-4.7-any)sgsR_1.5.0.zip(r-4.6-any)sgsR_1.5.0.zip(r-4.5-any)
sgsR_1.5.0.tgz(r-4.6-any)sgsR_1.5.0.tgz(r-4.5-any)
sgsR_1.5.0.tar.gz(r-4.7-any)sgsR_1.5.0.tar.gz(r-4.6-any)
sgsR_1.5.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
sgsR/json (API)

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

Bug tracker:https://github.com/tgoodbody/sgsr/issues

Pkgdown/docs site:https://tgoodbody.github.io

On CRAN:

Conda:

7.39 score 54 stars 45 scripts 348 downloads 45 exports 58 dependencies

Last updated from:be8855e75e. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK207
source / vignettesOK265
linux-releaseOK226
macos-releaseOK181
macos-oldrelOK191
windows-develOK165
windows-releaseOK146
windows-oldrelOK154
wasm-releaseOK132

Exports:ahels_nSampahels_thresholdallocate_equalallocate_existingallocate_existing_equalallocate_existing_manualallocate_existing_optimallocate_existing_propallocate_forceallocate_manualallocate_optimallocate_propcalculate_allocationcalculate_coobscalculate_distancecalculate_lhsOptcalculate_pcompcalculate_popcalculate_representationcalculate_sampsizeclassPlotextract_metricsextract_stratamask_accessmask_existingmat_covmat_covNBmat_quantplot_scattersample_ahelssample_balancedsample_clhssample_existingsample_ncsample_srssample_stratsample_sys_stratsample_systematicstrat_breaksstrat_kmeansstrat_mapstrat_polystrat_quantilesstrat_rule1strat_rule2

Dependencies:BalancedSamplingclassclassIntclhscliclustercpp11DBIdeldirdplyre1071farvergenericsggplot2gluegtableisobandKernSmoothlabelinglatticelifecyclemagrittrMatrixpillarpkgconfigplyrpolyclipproxypurrrR6rasterRColorBrewerRcppRcppArmadilloreshape2rlangs2S7SamplingBigDatascalessfspspatstat.dataspatstat.geomspatstat.univarspatstat.utilsstringistringrterratibbletidyrtidyselectunitsutf8vctrsviridisLitewithrwk

Sampling
sample_srs | sample_systematic | sample_strat | method = "Queinnec" | method = "random | sample_sys_strat | sample_nc | sample_clhs | sample_balanced | sample_ahels | sample_existing | sample_existing(type = "clhs") | sample_existing(type = "balanced") | sample_existing(type = "srs") | sample_existing(type = "strat")
Rendered fromsampling.Rmdusingknitr::rmarkdown

Last update: 2025-06-18
Started: 2022-01-27

Calculating
calculate_representation() | calculate_distance | calculate_pcomp | calculate_sampsize | calculate_allocation | Proportional allocation | Optimal Allocation | Equal allocation | Manual allocation | Sample evaluation algorithms | calculate_coobs | Latin hypercube sampling evaluation algorithms | calculate_pop | calculate_lhsOpt
Rendered fromcalculating.Rmdusingknitr::rmarkdown

Last update: 2023-06-09
Started: 2022-01-27

sgsR
Algorithm structure | strat_* | sample_* | calculate_* & extract_* | Parameters | mraster | sraster | access | %>%
Rendered fromsgsR.Rmdusingknitr::rmarkdown

Last update: 2023-02-08
Started: 2022-01-27

Stratification
strat_kmeans | strat_quantiles | strat_breaks | strat_poly | strat_map
Rendered fromstratification.Rmdusingknitr::rmarkdown

Last update: 2023-02-08
Started: 2022-01-27