Package: provenance 4.3

provenance: Statistical Toolbox for Sedimentary Provenance Analysis

Bundles a number of established statistical methods to facilitate the visual interpretation of large datasets in sedimentary geology. Includes functionality for adaptive kernel density estimation, principal component analysis, correspondence analysis, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov, Kuiper, Wasserstein-2 or Sircombe-Hazelton L2 distances. Categorical provenance proxies such as chemical compositions are compared with the Aitchison and Bray-Curtis distances,and count data with the chi-square distance. Varietal data can either be converted to one or more distributional datasets, or directly compared using the multivariate Wasserstein distance. Also included are tools to plot compositional and count data on ternary diagrams and point-counting data on radial plots, to calculate the sample size required for specified levels of statistical precision, and to assess the effects of hydraulic sorting on detrital compositions. Includes an intuitive query-based user interface for users who are not proficient in R.

Authors:Pieter Vermeesch [aut, cre]

provenance_4.3.tar.gz
provenance_4.3.zip(r-4.5)provenance_4.3.zip(r-4.4)provenance_4.3.zip(r-4.3)
provenance_4.3.tgz(r-4.4-any)provenance_4.3.tgz(r-4.3-any)
provenance_4.3.tar.gz(r-4.5-noble)provenance_4.3.tar.gz(r-4.4-noble)
provenance_4.3.tgz(r-4.4-emscripten)provenance_4.3.tgz(r-4.3-emscripten)
provenance.pdf |provenance.html✨
provenance/json (API)

# Install 'provenance' in R:
install.packages('provenance', repos = c('https://pvermees.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/pvermees/provenance/issues

Datasets:
  • Namib - An example dataset
  • SNSM - Varietal data example
  • densities - A list of rock and mineral densities
  • endmembers - Petrographic end-member compositions

On CRAN:

37 exports 14 stars 2.02 score 2 dependencies 1 dependents 63 scripts 483 downloads

Last updated 5 months agofrom:12b8c062a1. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 02 2024
R-4.5-winOKSep 02 2024
R-4.5-linuxOKSep 02 2024
R-4.4-winOKSep 02 2024
R-4.4-macOKSep 02 2024
R-4.3-winOKSep 02 2024
R-4.3-macOKSep 02 2024

Exports:ALRamalgamateas.acompas.compositionalas.countsas.varietalbotevbray.dissCACLRcombineget.fget.nget.pGPAindscalKDEKDEsKS.dissKuiper.dissMDSminsortingPCAprocrustesprovenanceread.compositionalread.countsread.densitiesread.distributionalread.varietalrestoreSH.disssummaryplotternaryternary.ellipsevarietal2distributionalWasserstein.diss

Dependencies:IsoplotRMASS

Readme and manuals

Help Manual

Help pageTopics
Additive logratio transformationALR ALR.compositional ALR.default
Group components of a compositionamalgamate amalgamate.compositional amalgamate.counts amalgamate.default amalgamate.SRDcorrected amalgamate.varietal
create an 'acomp' objectas.acomp
create a 'compositional' objectas.compositional
create a 'counts' objectas.counts
create a 'data.frame' objectas.data.frame as.data.frame.compositional as.data.frame.counts
create a 'varietal' objectas.varietal
Compute the optimal kernel bandwidthbotev
Bray-Curtis dissimilaritybray.diss bray.diss.compositional bray.diss.default
Correspondence AnalysisCA
Calculate central compositionscentral.counts
Centred logratio transformationCLR CLR.compositional CLR.default
Combine samples of distributional datacombine
A list of rock and mineral densitiesdensities
Calculate the dissimilarity matrix between two datasets of class 'distributional', 'compositional', 'counts' or 'varietal'diss.compositional diss.counts diss.distributional diss.varietal
Petrographic end-member compositionsendmembers
Calculate the largest fraction that is likely to be missedget.f
Calculate the number of grains required to achieve a desired level of sampling resolutionget.n
Calculate the probability of missing a given population fractionget.p
Generalised Procrustes Analysis of configurationsGPA
Individual Differences Scaling of provenance dataindscal
Create a kernel density estimateKDE
Generate an object of class 'KDEs'KDEs
Kolmogorov-Smirnov dissimilarityKS.diss KS.diss.default KS.diss.distributional
Kuiper dissimilarityKuiper.diss Kuiper.diss.default Kuiper.diss.distributional
Ternary line plottinglines.ternary
Multidimensional ScalingMDS MDS.compositional MDS.counts MDS.default MDS.distributional MDS.varietal
Assess settling equivalence of detrital componentsminsorting
An example datasetNamib
Principal Component AnalysisPCA
Point-counting biplotplot.CA
Plot a pie chartplot.compositional
Plot continuous data as histograms or cumulative age distributionsplot.distributional
Plot a Procrustes configurationplot.GPA
Plot an INDSCAL group configuration and source weightsplot.INDSCAL
Plot a kernel density estimateplot.KDE
Plot one or more kernel density estimatesplot.KDEs
Plot an MDS configurationplot.MDS
Plot inferred grain size distributionsplot.minsorting
Compositional biplotplot.PCA
Plot a ternary diagramplot.ternary
Ternary point plottinglines points.ternary text
Generalised Procrustes Analysis of provenance dataprocrustes
Menu-based interface for 'provenance'provenance-package provenance
Visualise point-counting data on a radial plotradialplot.counts
Read a .csv file with compositional dataread.compositional
Read a .csv file with point-counting dataread.counts
Read a .csv file with mineral and rock densitiesread.densities
Read a .csv file with distributional dataread.distributional
Read a .csv file with varietal dataread.varietal
Undo the effect of hydraulic sortingrestore
Sircombe and Hazelton distanceSH.diss
varietal data exampleSNSM
Get a subset of provenance datasubset subset.compositional subset.counts subset.distributional subset.varietal
Joint plot of several provenance datasetssummaryplot
Define a ternary compositionternary
Ternary confidence ellipseternary.ellipse ternary.ellipse.compositional ternary.ellipse.counts ternary.ellipse.default
Ternary text plottingtext.ternary
Convert varietal to distributional datavarietal2distributional
Wasserstein distanceWasserstein.diss Wasserstein.diss.default Wasserstein.diss.distributional Wasserstein.diss.varietal