Package: funrar 1.5.0

funrar: Functional Rarity Indices Computation

Computes functional rarity indices as proposed by Violle et al. (2017) <doi:10.1016/j.tree.2017.02.002>. Various indices can be computed using both regional and local information. Functional Rarity combines both the functional aspect of rarity as well as the extent aspect of rarity. 'funrar' is presented in Grenié et al. (2017) <doi:10.1111/ddi.12629>.

Authors:Matthias Grenié [aut, cre], Pierre Denelle [aut], Caroline Tucker [aut], François Munoz [ths], Cyrille Violle [ths]

funrar_1.5.0.tar.gz
funrar_1.5.0.zip(r-4.5)funrar_1.5.0.zip(r-4.4)funrar_1.5.0.zip(r-4.3)
funrar_1.5.0.tgz(r-4.4-any)funrar_1.5.0.tgz(r-4.3-any)
funrar_1.5.0.tar.gz(r-4.5-noble)funrar_1.5.0.tar.gz(r-4.4-noble)
funrar_1.5.0.tgz(r-4.4-emscripten)funrar_1.5.0.tgz(r-4.3-emscripten)
funrar.pdf |funrar.html
funrar/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/rekyt/funrar/issues

On CRAN:

ecological-modelsecologyraritytraits

7.90 score 17 stars 1 packages 257 scripts 434 downloads 1 mentions 25 exports 3 dependencies

Last updated 7 months agofrom:bf8a1ed53e. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 08 2024
R-4.5-winOKNov 08 2024
R-4.5-linuxOKNov 08 2024
R-4.4-winOKNov 08 2024
R-4.4-macOKNov 08 2024
R-4.3-winOKNov 08 2024
R-4.3-macOKNov 08 2024

Exports:compute_dist_matrixdistinctivenessdistinctiveness_altdistinctiveness_comdistinctiveness_dimensionsdistinctiveness_globaldistinctiveness_rangedistinctiveness_stackdistinctiveness_tidyfunrarfunrar_stackmake_relativematrix_to_stackrestrictednessrestrictedness_stackrestrictedness_tidyscarcityscarcity_comscarcity_stackscarcity_tidystack_to_matrixuniquenessuniqueness_dimensionsuniqueness_stackuniqueness_tidy

Dependencies:clusterlatticeMatrix

Alternative Distinctiveness definition

Rendered fromnew_distinctiveness.Rmdusingknitr::rmarkdownon Nov 08 2024.

Last update: 2022-08-30
Started: 2018-08-04

Introduction to funrar through an example

Rendered fromfunrar.Rmdusingknitr::rmarkdownon Nov 08 2024.

Last update: 2022-09-01
Started: 2022-09-01

Other functions of interest

Rendered fromother_functions.Rmdusingknitr::rmarkdownon Nov 08 2024.

Last update: 2022-09-23
Started: 2022-09-23

Sparse Matrices within funrar

Rendered fromsparse_matrices.Rmdusingknitr::rmarkdownon Nov 08 2024.

Last update: 2022-09-01
Started: 2016-07-06

Readme and manuals

Help Manual

Help pageTopics
Compute Multiple distance matrices from a single trait tablecombination_trait_dist
Compute a Functional Dissimilarity Matrixcompute_dist_matrix distance_matrix
Functional Distinctiveness on site-species matrixdistinctiveness
Truncated Functional Distinctivenessdistinctiveness_alt
Functional Distinctiveness for a single communitydistinctiveness_com
Distinctiveness across combinations of traitsdistinctiveness_dimensions
Global/Regional Functional Distinctiveness from dissimilarity matrixdistinctiveness_global
Alternative Truncated Functional Distinctivenessdistinctiveness_range
Functional Distinctiveness on a stacked data.framedistinctiveness_stack distinctiveness_tidy
Compute all Functional Rarity Indices from Matricesfunrar
Compute all Functional Rarity Indices from stacked data.framesfunrar_stack
Tell if matrix or data.frame has relative abundancesis_relative
Relative abundance matrix from absolute abundance matrixmake_relative
Matrix to stacked (= tidy) data.framematrix_to_stack matrix_to_tidy
Geographical Restrictedness on site-species matrixrestrictedness
Geographical Restrictedness for stacked data.framerestrictedness_stack restrictedness_tidy
Scarcity on site-species matrixscarcity
Scarcity for a single communityscarcity_com
Scarcity on a stacked data.framescarcity_stack scarcity_tidy
Stacked (= tidy) data.frame to matrixstack_to_matrix tidy_to_matrix
Functional Uniqueness for site-species matrix matrixuniqueness
Uniqueness across combinations of traitsuniqueness_dimensions
Functional Uniqueness on stacked data.frameuniqueness_stack uniqueness_tidy