Package: TAD 1.0.0

Lain Pavot

TAD: Realize the Trait Abundance Distribution

This analytical framework is based on an analysis of the shape of the trait abundance distributions to better understand community assembly processes, and predict community dynamics under environmental changes. This framework mobilized a study of the relationship between the moments describing the shape of the distributions: the skewness and the kurtosis (SKR). The SKR allows the identification of commonalities in the shape of trait distributions across contrasting communities. Derived from the SKR, we developed mathematical parameters that summarise the complex pattern of distributions by assessing (i) the R², (ii) the Y-intercept, (iii) the slope, (iv) the functional stability of community (TADstab), and, (v) the distance from specific distribution families (i.e., the distance from the skew-uniform family a limit to the highest degree of evenness: TADeve).

Authors:Nathan Rondeau [aut], Yoann Le Bagousse-Pinguet [aut], Raphael Martin [aut], Lain Pavot [aut, cre], Pierre Liancourt [aut], Nicolas Gross [aut], INRAe/UREP [cph]

TAD_1.0.0.tar.gz
TAD_1.0.0.tar.gz(r-4.5-noble)TAD_1.0.0.tar.gz(r-4.4-noble)
TAD_1.0.0.tgz(r-4.4-emscripten)TAD_1.0.0.tgz(r-4.3-emscripten)
TAD.pdf |TAD.html
TAD/json (API)
NEWS

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

Peer review:

Datasets:

    This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

    2.60 score 18 exports 11 dependencies

    Last updated 13 days agofrom:1821aa6069. Checks:OK: 2. Indexed: no.

    TargetResultDate
    Doc / VignettesOKNov 28 2024
    R-4.5-linuxOKNov 28 2024

    Exports:CONSTANTSgenerate_random_matrixlaunch_analysis_tadload_abundance_dataframeload_stat_skr_paramload_statistics_per_obsload_statistics_per_randomload_weighted_momentsmoments_graphnull_model_distribution_statssave_abundance_dataframesave_stat_skr_paramsave_statistics_per_obssave_statistics_per_randomsave_weighted_momentsskr_graphskr_param_graphweighted_mvsk

    Dependencies:codetoolsdigestdoFutureforeachfuturefuture.applyglobalsiteratorslistenvmblmparallelly

    Best strategy

    Rendered frombest-strategy.Rmdusingknitr::rmarkdownon Nov 28 2024.

    Last update: 2024-11-28
    Started: 2024-11-28

    Get outputs in different formats

    Rendered fromoutput-different-formats.Rmdusingknitr::rmarkdownon Nov 28 2024.

    Last update: 2024-11-28
    Started: 2024-11-28

    graph-outputs

    Rendered fromgraph-outputs.Rmdusingknitr::rmarkdownon Nov 28 2024.

    Last update: 2024-11-28
    Started: 2024-11-28

    Multiprocessing and single-core processing

    Rendered fromgeneral-use-of-tad.Rmdusingknitr::rmarkdownon Nov 28 2024.

    Last update: 2024-11-28
    Started: 2024-11-28

    Readme and manuals

    Help Manual

    Help pageTopics
    The CONSTANTS constantCONSTANTS
    Generate random matrixgenerate_random_matrix
    Launch the analysislaunch_analysis_tad
    load_abundance_dataframeload_abundance_dataframe
    load_stat_skr_paramload_stat_skr_param
    load_statistics_per_obsload_statistics_per_obs
    load_statistics_per_randomload_statistics_per_random
    load_weighted_momentsload_weighted_moments
    moments_graphmoments_graph
    Compare a value to random valuesnull_model_distribution_stats
    save_abundance_dataframesave_abundance_dataframe
    save_stat_skr_paramsave_stat_skr_param
    save_statistics_per_obssave_statistics_per_obs
    save_statistics_per_randomsave_statistics_per_random
    save_weighted_momentssave_weighted_moments
    skr_graphskr_graph
    skr_param_graphskr_param_graph
    Compute the weighted mean, variance, skewness and kurtosisweighted_mvsk