Statistical Toolbox for Sedimentary Provenance Analysis


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Documentation for package ‘provenance’ version 4.3

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provenance-package Menu-based interface for 'provenance'
ALR Additive logratio transformation
ALR.compositional Additive logratio transformation
ALR.default Additive logratio transformation
amalgamate Group components of a composition
amalgamate.compositional Group components of a composition
amalgamate.counts Group components of a composition
amalgamate.default Group components of a composition
amalgamate.SRDcorrected Group components of a composition
amalgamate.varietal Group components of a composition
as.acomp create an 'acomp' object
as.compositional create a 'compositional' object
as.counts create a 'counts' object
as.data.frame create a 'data.frame' object
as.data.frame.compositional create a 'data.frame' object
as.data.frame.counts create a 'data.frame' object
as.varietal create a 'varietal' object
botev Compute the optimal kernel bandwidth
bray.diss Bray-Curtis dissimilarity
bray.diss.compositional Bray-Curtis dissimilarity
bray.diss.default Bray-Curtis dissimilarity
CA Correspondence Analysis
central.counts Calculate central compositions
CLR Centred logratio transformation
CLR.compositional Centred logratio transformation
CLR.default Centred logratio transformation
combine Combine samples of distributional data
densities A list of rock and mineral densities
diss.compositional Calculate the dissimilarity matrix between two datasets of class 'distributional', 'compositional', 'counts' or 'varietal'
diss.counts Calculate the dissimilarity matrix between two datasets of class 'distributional', 'compositional', 'counts' or 'varietal'
diss.distributional Calculate the dissimilarity matrix between two datasets of class 'distributional', 'compositional', 'counts' or 'varietal'
diss.varietal Calculate the dissimilarity matrix between two datasets of class 'distributional', 'compositional', 'counts' or 'varietal'
endmembers Petrographic end-member compositions
get.f Calculate the largest fraction that is likely to be missed
get.n Calculate the number of grains required to achieve a desired level of sampling resolution
get.p Calculate the probability of missing a given population fraction
GPA Generalised Procrustes Analysis of configurations
indscal Individual Differences Scaling of provenance data
KDE Create a kernel density estimate
KDEs Generate an object of class 'KDEs'
KS.diss Kolmogorov-Smirnov dissimilarity
KS.diss.default Kolmogorov-Smirnov dissimilarity
KS.diss.distributional Kolmogorov-Smirnov dissimilarity
Kuiper.diss Kuiper dissimilarity
Kuiper.diss.default Kuiper dissimilarity
Kuiper.diss.distributional Kuiper dissimilarity
lines Ternary point plotting
lines.ternary Ternary line plotting
MDS Multidimensional Scaling
MDS.compositional Multidimensional Scaling
MDS.counts Multidimensional Scaling
MDS.default Multidimensional Scaling
MDS.distributional Multidimensional Scaling
MDS.varietal Multidimensional Scaling
minsorting Assess settling equivalence of detrital components
Namib An example dataset
PCA Principal Component Analysis
plot.CA Point-counting biplot
plot.compositional Plot a pie chart
plot.distributional Plot continuous data as histograms or cumulative age distributions
plot.GPA Plot a Procrustes configuration
plot.INDSCAL Plot an INDSCAL group configuration and source weights
plot.KDE Plot a kernel density estimate
plot.KDEs Plot one or more kernel density estimates
plot.MDS Plot an MDS configuration
plot.minsorting Plot inferred grain size distributions
plot.PCA Compositional biplot
plot.ternary Plot a ternary diagram
points.ternary Ternary point plotting
procrustes Generalised Procrustes Analysis of provenance data
provenance Menu-based interface for 'provenance'
radialplot.counts Visualise point-counting data on a radial plot
read.compositional Read a .csv file with compositional data
read.counts Read a .csv file with point-counting data
read.densities Read a .csv file with mineral and rock densities
read.distributional Read a .csv file with distributional data
read.varietal Read a .csv file with varietal data
restore Undo the effect of hydraulic sorting
SH.diss Sircombe and Hazelton distance
SNSM varietal data example
subset Get a subset of provenance data
subset.compositional Get a subset of provenance data
subset.counts Get a subset of provenance data
subset.distributional Get a subset of provenance data
subset.varietal Get a subset of provenance data
summaryplot Joint plot of several provenance datasets
ternary Define a ternary composition
ternary.ellipse Ternary confidence ellipse
ternary.ellipse.compositional Ternary confidence ellipse
ternary.ellipse.counts Ternary confidence ellipse
ternary.ellipse.default Ternary confidence ellipse
text Ternary point plotting
text.ternary Ternary text plotting
varietal2distributional Convert varietal to distributional data
Wasserstein.diss Wasserstein distance
Wasserstein.diss.default Wasserstein distance
Wasserstein.diss.distributional Wasserstein distance
Wasserstein.diss.varietal Wasserstein distance