R: Statistical Toolbox for Sedimentary Provenance Analysis

# Statistical Toolbox for Sedimentary Provenance Analysis

## Documentation for package ‘provenance’ version 4.4

- DESCRIPTION file
- Package README

## Help Pages

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