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 |