Statistical Toolbox for Sedimentary Provenance Analysis
Generalised Procrustes Analysis of configurations
Calculate the dissimilarity matrix between two datasets of class `dist...
Calculate the largest fraction that is likely to be missed
Calculate central compositions
Centred logratio transformation
Combine samples of distributional data
Calculate the number of grains required to achieve a desired level of ...
Calculate the probability of missing a given population fraction
Additive logratio transformation
Group components of a composition
create an acomp object
create a compositional object
create a counts object
create a data.frame object
create a varietal object
Compute the optimal kernel bandwidth
Bray-Curtis dissimilarity
Correspondence Analysis
Individual Differences Scaling of provenance data
Create a kernel density estimate
Generate an object of class KDEs
Kolmogorov-Smirnov dissimilarity
Kuiper dissimilarity
Ternary line plotting
Multidimensional Scaling
Assess settling equivalence of detrital components
Principal Component Analysis
Point-counting biplot
Plot a pie chart
Plot continuous data as histograms or cumulative age distributions
Plot a Procrustes configuration
Plot an INDSCAL group configuration and source weights
Plot a kernel density estimate
Plot one or more kernel density estimates
Plot an MDS configuration
Plot inferred grain size distributions
Compositional biplot
Plot a ternary diagram
Ternary point plotting
Generalised Procrustes Analysis of provenance data
Menu-based interface for provenance
Visualise point-counting data on a radial plot
Read a .csv file with compositional data
Read a .csv file with point-counting data
Read a .csv file with mineral and rock densities
Read a .csv file with distributional data
Read a .csv file with varietal data
Undo the effect of hydraulic sorting
Sircombe and Hazelton distance
Get a subset of provenance data
Joint plot of several provenance datasets
Ternary confidence ellipse
Define a ternary composition
Ternary text plotting
Convert varietal to distributional data
Wasserstein distance
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.