A Comprehensive Package for Sediment Source Unmixing

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Documentation for package ‘FluvialTracer’ version 1.0.0

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boxPlot Box and whiskers plot
box_plot Box and whiskers plot
CB_Method Conservative Balance (CB) transformation for isotopic tracers
cb_method Conservative Balance (CB) transformation for isotopic tracers
checking_diff_mult_mix Checking the difference between virtual and artificial mixture (for data sets containing multiple mixtures)
checking_diff_single_mix Checking the difference between virtual and artificial mixture (for data sets containing only one mixture)
CI_Method Conservativeness Index (CI) for individual tracer evaluation
cluster_solutions Cluster CTS solutions by source contribution similarity
Conservative_Balance Conservative Balance (CB) transformation for isotopic tracers
consistency_error Consistency error of a tracer selection for an apportionment solution
correlationPlot Correlation matrix chart
Correlation_Chart Correlation matrix chart
correlation_plot Correlation matrix chart
CR Consensus Ranking (CR) method for tracer selection
CR_Method Consensus Ranking (CR) method for tracer selection
cr_ns Consensus Ranking (CR) method for tracer selection
CTS_2_sources CTS engine for 2 sources
CTS_3_sources CTS engine for 3 sources
CTS_4_sources CTS engine for 4 sources
CTS_5_sources CTS engine for 5 sources
CTS_error Consistency error of a tracer selection for an apportionment solution
cts_error Consistency error of a tracer selection for an apportionment solution
CTS_n_sources Consistent Tracer Selection (CTS) for multiple sources
CTS_seeds Consistent Tracer Selection (CTS) for multiple sources
CTS_seeds_pairs CTS engine for 3 sources
CTS_seeds_quartets CTS engine for 5 sources
CTS_seeds_singles CTS engine for 2 sources
CTS_seeds_triplets CTS engine for 4 sources
DFATest Discriminant Function Analysis for tracer pre-selection
fluvialdata_example Example dataset with 3 sources and one mixture
fluvial_palette Default color palette for FluvialTracer plots
frequentist_unconstrained Source apportionment via unconstrained least-squares with classical variability propagation
frequentist_unconstrained_lvp Source apportionment via unconstrained least-squares with linear variability propagation (LVP)
ggbiplot Biplot for Principal Components using ggplot2
Graph_boxplots Box and whiskers plot
inputMixture Extract mixture samples from a fingerprinting dataset
inputSample Extract mixture samples from a fingerprinting dataset
inputSource Input sediment sources
KWTest Kruskal-Wallis test for tracer pre-selection
LDAPlot Linear discriminant analysis chart
LDA_Chart Linear discriminant analysis chart
LDA_plot Linear discriminant analysis chart
pairs CTS engine for 3 sources
PCAPlot Principal Component Analysis biplot
PCA_plot Principal Component Analysis biplot
plotResults Plot apportionment results
plot_apportionment Plot apportionment results
plot_pca Principal Component Analysis biplot
plot_results Plot apportionment results
quartets CTS engine for 5 sources
rangeTest Range test for tracer pre-selection
singles CTS engine for 2 sources
Ternary_diagram Ternary diagrams with the CI_Method function results
ternary_diagram Ternary diagrams with the CI_Method function results
Ternary_Plot Ternary diagrams with the CI_Method function results
tracer_pull_analysis Analyze the Marginal Effect (Pull) of Individual Tracers
triangles_dataset Conservative Index evaluation using randomly sampled companion tracers
triangles_virtual Conservative Index evaluation using virtually generated companion tracers
triplets CTS engine for 4 sources
unmixing Unmix sediment or marine particle mixtures
unmix_mixsiar Bayesian source apportionment using MixSIAR
unmix_rf Machine Learning source apportionment using Random Forest (with expanded bounds)
unmix_xgboost Machine Learning source apportionment using XGBoost (Gradient Boosting)