CI_Method               Conservativeness Index (CI) for individual
                        tracer evaluation
CR_Method               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_n_sources           Consistent Tracer Selection (CTS) for multiple
                        sources
Conservative_Balance    Conservative Balance (CB) transformation for
                        isotopic tracers
Correlation_Chart       Correlation matrix chart
DFATest                 Discriminant Function Analysis for tracer
                        pre-selection
Graph_boxplots          Box and whiskers plot
KWTest                  Kruskal-Wallis test for tracer pre-selection
LDA_Chart               Linear discriminant analysis chart
Ternary_Plot            Ternary diagrams with the CI_Method function
                        results
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)
cluster_solutions       Cluster CTS solutions by source contribution
                        similarity
consistency_error       Consistency error of a tracer selection for an
                        apportionment solution
fluvial_palette         Default color palette for FluvialTracer plots
fluvialdata_example     Example dataset with 3 sources and one mixture
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
inputSample             Extract mixture samples from a fingerprinting
                        dataset
inputSource             Input sediment sources
plot_apportionment      Plot apportionment results
plot_pca                Principal Component Analysis biplot
rangeTest               Range test for tracer pre-selection
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
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)
unmixing                Unmix sediment or marine particle mixtures
