Package: FluvialTracer 1.0.0

Margarita Villalonga

FluvialTracer: A Comprehensive Package for Sediment Source Unmixing

Source apportionment toolkit for fluvial catchments and river sediment provenance. It estimates how much each source contributes to a sediment mixture, using frequentist, Bayesian, and machine-learning models on the same dataset. The software includes tools to check which tracer is causing an unrealistic result, plus built-in validation against artificial mixtures with known, true source proportions.

Authors:Margarita Villalonga [aut, cre], Esther Cepeda [aut], Maarten Wynants [aut], Ivan Lizaga [aut, ths, ldr], Samuel Bodé [ctb], Pascal Boeckx [ctb], William Blake [ctb], Jan Vanaverbeke [ctb], Ulrike Braeckman [ctb], Aiming Lian [ctb], Borja Latorre [ctb], Yuri Jacques Agra Bezerra da Silva [ctb], Leticia Gaspar [ctb], Jorge Crespo [ctb], Ana Navas [ctb], Maziar Mohammadi [ctb], Vince Q Vu [ctb]

FluvialTracer_1.0.0.tar.gz
FluvialTracer_1.0.0.zip(r-4.7-any)FluvialTracer_1.0.0.zip(r-4.6-any)
FluvialTracer_1.0.0.tar.gz(r-4.7-any)FluvialTracer_1.0.0.tar.gz(r-4.6-any)
FluvialTracer_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
FluvialTracer/json (API)

# Install 'FluvialTracer' in R:
install.packages('FluvialTracer', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

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

jagscpp

1.00 score 61 exports 176 dependencies

Last updated from:fa7871ade8. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK185
source / vignettesOK223
linux-releaseOK179
windows-develOK256
windows-releaseOK270
wasm-releaseOK152

Exports:box_plotboxPlotcb_methodCB_Methodchecking_diff_mult_mixchecking_diff_single_mixCI_Methodcluster_solutionsConservative_Balanceconsistency_errorCorrelation_Chartcorrelation_plotcorrelationPlotCRCR_Methodcr_nsCTS_2_sourcesCTS_3_sourcesCTS_4_sourcesCTS_5_sourcescts_errorCTS_errorCTS_n_sourcesCTS_seedsCTS_seeds_pairsCTS_seeds_quartetsCTS_seeds_singlesCTS_seeds_tripletsDFATestfrequentist_unconstrainedfrequentist_unconstrained_lvpggbiplotGraph_boxplotsinputMixtureinputSampleinputSourceKWTestLDA_ChartLDA_plotLDAPlotpairsPCA_plotPCAPlotplot_apportionmentplot_pcaplot_resultsplotResultsquartetsrangeTestsinglesternary_diagramTernary_diagramTernary_Plottracer_pull_analysistriangles_datasettriangles_virtualtripletsunmix_mixsiarunmix_rfunmix_xgboostunmixing

Dependencies:abindaskpassbackportsbase64encbayesplotbitbit64bootbroombslibcachemcarcarDatacheckmateclassclassIntclicliprclueclustercodacolorspacecombinatcommonmarkcowplotcpp11crayoncrosstalkcurldata.tableDerivdigestdistributionaldoBydplyre1071evaluatefarverfastmapfontawesomeforcatsforecastFormulafracdifffsgenericsGGallyggdistggmcmcggplot2ggridgesggstatsggtextgluegridExtragridtextgtablehavenhighrhmshtmltoolshtmlwidgetshttpuvhttrisobandjpegjquerylibjsonliteKernSmoothklaRknitrlabelinglabelledlaterlatticelazyevallifecyclelitedownlme4lmtestloomagrittrmarkdownMASSMatrixMatrixModelsmatrixStatsmcmcMCMCpackmemoisemgcvmimeminiUIminqaMixSIARmodelrnlmenloptrnnetnumDerivopensslotelpatchworkpbkrtestpillarpkgconfigplotlyPlotToolsplyrpngposteriorprettyunitsprogresspromisesproxypurrrquadprogquantregquestionrR.cacheR.methodsS3R.ooR.utilsR2jagsR2WinBUGSR6randomForestrappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreadrreformulasreshapereshape2rjagsrlangrmarkdownrprojrootrstudioapiS7sassscalesshinysourcetoolsspSparseMsplancsstringistringrstylersurvivalsystensorATernarytibbletidyrtidyselecttimeDatetinytextzdburcautf8vctrsviridisLitevroomwithrxfunxgboostxml2xtableyamlzoo

Readme and manuals

Help Manual

Help pageTopics
Checking the difference between virtual and artificial mixture (for data sets containing multiple mixtures)checking_diff_mult_mix
Checking the difference between virtual and artificial mixture (for data sets containing only one mixture)checking_diff_single_mix
Conservativeness Index (CI) for individual tracer evaluationCI_Method
Cluster CTS solutions by source contribution similaritycluster_solutions
Conservative Balance (CB) transformation for isotopic tracersCB_Method cb_method Conservative_Balance
Consistency error of a tracer selection for an apportionment solutionconsistency_error CTS_error cts_error
Correlation matrix chartcorrelationPlot Correlation_Chart correlation_plot
Consensus Ranking (CR) method for tracer selectionCR CR_Method cr_ns
CTS engine for 2 sourcesCTS_2_sources CTS_seeds_singles singles
CTS engine for 3 sourcesCTS_3_sources CTS_seeds_pairs pairs
CTS engine for 4 sourcesCTS_4_sources CTS_seeds_triplets triplets
CTS engine for 5 sourcesCTS_5_sources CTS_seeds_quartets quartets
Consistent Tracer Selection (CTS) for multiple sourcesCTS_n_sources CTS_seeds
Discriminant Function Analysis for tracer pre-selectionDFATest
Default color palette for FluvialTracer plotsfluvial_palette
Example dataset with 3 sources and one mixturefluvialdata_example
Source apportionment via unconstrained least-squares with classical variability propagationfrequentist_unconstrained
Source apportionment via unconstrained least-squares with linear variability propagation (LVP)frequentist_unconstrained_lvp
Biplot for Principal Components using ggplot2ggbiplot
Box and whiskers plotboxPlot box_plot Graph_boxplots
Extract mixture samples from a fingerprinting datasetinputMixture inputSample
Input sediment sourcesinputSource
Kruskal-Wallis test for tracer pre-selectionKWTest
Linear discriminant analysis chartLDAPlot LDA_Chart LDA_plot
Plot apportionment resultsplotResults plot_apportionment plot_results
Principal Component Analysis biplotPCAPlot PCA_plot plot_pca
Range test for tracer pre-selectionrangeTest
Ternary diagrams with the CI_Method function resultsTernary_diagram ternary_diagram Ternary_Plot
Analyze the Marginal Effect (Pull) of Individual Tracerstracer_pull_analysis
Conservative Index evaluation using randomly sampled companion tracerstriangles_dataset
Conservative Index evaluation using virtually generated companion tracerstriangles_virtual
Bayesian source apportionment using MixSIARunmix_mixsiar
Machine Learning source apportionment using Random Forest (with expanded bounds)unmix_rf
Machine Learning source apportionment using XGBoost (Gradient Boosting)unmix_xgboost
Unmix sediment or marine particle mixturesunmixing