Package: bean 0.2.2

Paanwaris Paansri

bean: Data Thinning of Species Occurrences in Environmental Space

A suite of tools to mitigate sampling bias in species occurrence records by thinning data in the environmental space (E-space). This process can improve the accuracy and precision of species distribution models (SDM, also known as ecological niche models, ENM). The package offers a data-driven protocol to determine thinning parameters using kernel-density bandwidth selection. Two thinning methods are provided (stochastic and deterministic) to reduce over-sampled environmental conditions and down-weight outlier observations. The name 'bean' reflects the core principle of the method: each 'pod' (a grid cell in E-space) is allowed to contain only a limited number of 'beans' (occurrence points). See Silverman (1986, ISBN:978-0-412-24620-3) and Rousseeuw and Leroy (2003, ISBN:978-0-471-48855-2) for the underlying statistical methods.

Authors:Paanwaris Paansri [cre, aut], Luis E. Escobar [aut, ctb]

bean_0.2.2.tar.gz
bean_0.2.2.zip(r-4.7)bean_0.2.2.zip(r-4.6)bean_0.2.2.zip(r-4.5)
bean_0.2.2.tgz(r-4.6-any)bean_0.2.2.tgz(r-4.5-any)
bean_0.2.2.tar.gz(r-4.7-any)bean_0.2.2.tar.gz(r-4.6-any)
bean_0.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
bean/json (API)

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

Bug tracker:https://github.com/paanwaris/bean/issues

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

Datasets:

On CRAN:

Conda:

ecological-niche-modellingspatial-analysis

5.95 score 4 stars 14 scripts 575 downloads 6 exports 3 dependencies

Last updated from:3f76b69fa6. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK198
source / vignettesOK211
linux-release-x86_64OK193
macos-release-arm64OK192
macos-oldrel-arm64OK151
windows-develOK164
windows-releaseOK152
windows-oldrelOK142
wasm-releaseOK138

Exports:find_env_resolutionfit_ellipsoidplot_beanprepare_beanthin_env_centerthin_env_nd

Dependencies:MASSRcppterra

bean: an overview
Quickstart

Last update: 2026-06-19
Started: 2026-05-26

Data preparation
Load raw data | Visualise the environmental layers | Visualise the raw occurrence points | Run prepare_bean() | Visualise the prepared points | The shipped, pre-computed dataset

Last update: 2026-06-19
Started: 2026-05-13

Environmental thinning
Choosing an objective grid resolution | The intuition | Why a kernel-density bandwidth? | Which selector? | Computing the resolution | Visualising the bandwidth | Sensitivity to the selector | Stochastic thinning | Deterministic thinning | Comparing the two thinned datasets | Pairs view with plot_bean()

Last update: 2026-06-19
Started: 2026-05-13

Niche modeling
Fit three ellipsoids | Visualise the ellipsoids (2-D slices) | Inspecting the inferred niche | Projecting suitability with nicheR | Installing and loading nicheR | Predicting suitability | References

Last update: 2026-06-19
Started: 2026-05-13