Package: invasimapr 0.2.0

Sandra MacFadyen

invasimapr: Workflow to Visualise Trait Dispersion and Assess Invasibility

Utilities for assembling invader-resident interaction tensors, computing trait-based competition kernels from distance matrices, deriving environmental suitability kernels, calculating invasion fitness, and summarising trait dispersion metrics. Includes helpers to predict site-by-species responses for residents and simulated invaders, and to derive species invasiveness, site invasibility and trait-effect indicators. The package implements a network invasibility framework that decomposes invasion fitness into abiotic suitability, niche crowding and resident competition components, following the ecological-network theory of Hui and colleagues.

Authors:Sandra MacFadyen [aut, cre], Stellenbosch University [cph]

invasimapr_0.2.0.tar.gz
invasimapr_0.2.0.zip(r-4.7)invasimapr_0.2.0.zip(r-4.6)invasimapr_0.2.0.zip(r-4.5)
invasimapr_0.2.0.tgz(r-4.6-any)invasimapr_0.2.0.tgz(r-4.5-any)
invasimapr_0.2.0.tar.gz(r-4.7-any)invasimapr_0.2.0.tar.gz(r-4.6-any)
invasimapr_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
invasimapr/json (API)

# Install 'invasimapr' in R:

install.packages('invasimapr', repos = c('https://b-cubed-eu.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/b-cubed-eu/invasimapr/issues

Pkgdown/docs site:https://b-cubed-eu.github.io

On CRAN:

3.90 score 1 stars 9 scripts 26 exports 139 dependencies

Last updated from:31a107a0e5. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	243
source / vignettes	OK	366
linux-release-x86_64	OK	253
macos-release-arm64	OK	169
macos-oldrel-arm64	OK	168
windows-devel	OK	160
windows-release	OK	162
windows-oldrel	OK	172
wasm-release	OK	254

Exports:assemble_matrices build_invader_predictors build_model_formula compute_centrality_hull compute_establishment_probability compute_invasion_fitness compute_resident_crowding compute_site_saturation compute_trait_space derive_sensitivities fit_auxiliary_residents_glmm get_trait_data invasimapr learn_sensitivities model_residents predict_establishment predict_invaders prep_resident_glmm prepare_inputs prepare_trait_space simulate_invaders site_varying_alpha_beta_gamma standardise_by_site standardise_model_inputs summarise_invasiveness_invasibility summarise_results

Dependencies:abind askpass backports base64enc boot broom bslib cachem car carData cli cluster colorspace corrplot cowplot cpp11 crosstalk curl data.table dendextend Deriv digest doBy dplyr DT ellipse emmeans estimability evaluate factoextra FactoMineR farver fastmap flashClust fontawesome forcats forecast Formula fracdiff fs fuzzyjoin generics geosphere ggplot2 ggpubr ggrepel ggsci ggsignif glmmTMB glue gridExtra gtable highr htmltools htmlwidgets httr irlba isoband jquerylib jsonlite knitr labeling later lattice lazyeval leaps lifecycle lme4 lmtest magick magrittr MASS Matrix MatrixModels matrixStats memoise mgcv microbenchmark mime minqa modelr multcompView mvtnorm nlme nloptr nnet numDeriv openssl otel pbkrtest permute pillar pkgconfig polynom promises purrr quantreg R6 rappdirs rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack reformulas rlang rmarkdown rstatix rvest S7 sandwich sass scales scatterplot3d selectr SparseM stringdist stringi stringr survival sys tibble tidyr tidyselect timeDate tinytex TMB urca utf8 vctrs vegan viridis viridisLite withr xfun xml2 yaml zoo

Getting started with invasimapr

Overview | Installation | The workflow | Where to next | Citing invasimapr

Rendered frominvasimapr.Rmdusingknitr::rmarkdown

Last update: 2026-06-27
Started: 2026-06-27

Help page	Topics
Assemble community matrices for invasion-fitness workflows	assemble_matrices
Build standardized invader predictors r_is_z, C_is_z, S_is_z (expects env/traits frames already projected to model columns)	build_invader_predictors
Flexible formula constructor for residents-only trait–environment models	build_model_formula
Compute trait-space centrality (robust Mahalanobis) and hull status, with visuals	compute_centrality_hull
Probabilistic establishment from invasion fitness	compute_establishment_probability
Compute invasion fitness lambda_{is} for multiple model options	compute_invasion_fitness
Resident crowding C_{js} via Hellinger composition and Gower–Gaussian trait kernel	compute_resident_crowding
Compute site-only saturation S_{s} and global z-score S_{s}^{(z)}	compute_site_saturation
Shared trait-space construction (Gower + PCoA), resident hull, centroid, and density plot	compute_trait_space
Derive trait-varying sensitivities (αᵢ, βᵢ) and abiotic slope (θ, Γᵢ)	derive_sensitivities
Auxiliary GLMM for trait-varying and site-varying slopes	fit_auxiliary_residents_glmm
Scrape and Analyze Wikipedia & Trait Data for a Species	get_trait_data
Learn sensitivities (alpha_i, beta_i, theta_i/gamma_i) and optional site-varying alpha_is, Gamma_is	learn_sensitivities
Resident GLMM and construction of standardized predictors	model_residents
Compute invasion fitness (lambda) and optional establishment probability (P)	predict_establishment
Build standardized invader predictors (r_is_z, C_is_z, S_is_z)	predict_invaders
Build residents-by-sites long table and fit the residents-only GLMM	prep_resident_glmm
Prepare inputs (assemble and align core tables)	prepare_inputs
Prepare trait space and resident crowding (no site-z)	prepare_trait_space
Simulate hypothetical invader trait profiles from a resident trait pool	simulate_invaders
Site-varying alpha and gamma with trait-dependent beta	site_varying_alpha_beta_gamma
Standardize a site-by-species matrix by site (row-wise z) with optional robust SD	standardise_by_site
Standardise model inputs (no leakage) for residents and invaders	standardise_model_inputs
Summaries of invasion fitness: species invasiveness, trait effects, and site invasibility	summarise_invasiveness_invasibility
Summarise invasiveness and invasibility (tables, maps, rankings)	summarise_results

Package: invasimapr 0.2.0

invasimapr: Workflow to Visualise Trait Dispersion and Assess Invasibility

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