Learn R Programming

⚠️There's a newer version (4.4) of this package.Take me there.

provenance (version 1.1)

Statistical Toolbox for Sedimentary Provenance Analysis

Description

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, 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 dissimilarity and Sircombe-Hazelton L2-norm. Categorical provenance proxies, such as mineralogical, petrographic or chemical compositions are compared with the Aitchison and Bray-Curtis distances. Also included are tools to plot compositional data on ternary diagrams, 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.

Copy Link

Version

Install

install.packages('provenance')

Monthly Downloads

328

Version

1.1

License

GPL-2

Maintainer

Pieter Vermeesch

Last Published

January 3rd, 2016

Functions in provenance (1.1)

as.acomp

create an acomp object
read.compositional

Read a .csv file with categorical data
read.densities

Read a .csv file with mineral and rock densities
plot.ternary

Plot a ternary diagram
ternary

Define a ternary composition
botev

Compute the optimal kernel bandwidth
summaryplot

Joint plot of several provenance datasets
CLR

Centred logratio transformation
KDEs

Generate an object of class KDEs
minsorting

Assess settling equivalence of detrital components
get.p

Calculate the probability of missing a given population fraction
bray.diss

Bray-Curtis dissimilarity
densities

A list of rock and mineral densities
as.compositional

create a compositional object
SH.diss

Sircombe and Hazelton distance
indscal

Individual Differences Scaling of provenance data
plot.PCA

Compositional biplot
subset.compositional

Get a subset of compositional data
procrustes

Generalised Procrustes Analysis of provenance data
provenance

Menu-based interface for provenance
as.data.frame.compositional

create a data.frame object
amalgamate

Group components of a composition
PCA

Principal Component Analysis
KDE

Create a kernel density estimate
get.n

Calculate the number of grains required to achieve a desired level of sampling resolution
diss

Calculate the dissimilarity matrix between two distributional or compositional datasets
plot.compositional

Plot a pie chart
get.f

Calculate the largest fraction that is likely to be missed
GPA

Generalised Procrustes Analysis of configurations
plot.MDS

Plot an MDS configuration
read.distributional

Read a .csv file with continuous (detrital zircon) data
subset.distributional

Get a subset of distributional data
plot.distributional

Plot continuous data as histograms or cumulative age distributions
plot.minsorting

Plot inferred grain size distributions
Namib

An example dataset
KS.diss

Kolmogorov-Smirnov dissimilarity
endmembers

Petrographic end-member compositions
MDS

Multidimensional Scaling
plot.GPA

Plot a Procrustes configuration
plot.INDSCAL

Plot an INDSCAL group configuration and source weights
restore

Undo the effect of hydraulic sorting
plot.KDE

Plot a kernel density estimate