**provenance** 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 or Sircombe-Hazelton L2 distances. Categorical provenance
proxies such as chemical compositions are compared with the
Aitchison and Bray-Curtis distances, and point-counting data with
the chi-square distance. Also included are tools to plot
compositional and point-counting 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..

You must have **R** installed on your system (see
https://www.r-project.org). Additionally, to
install provenance from Github, you also need the **devtools**
package. This can be installed by typing the following code at the R
command line prompt:

```
install.packages('devtools')
```

The most recent stable version of provenance is available from **CRAN** at
https://cran.r-project.org/package=provenance
and can be installed on your system as follows:

```
install.packages('provenance')
```

Alternatively, to install the current development version of provenance from Github, type:

```
library(devtools)
install_github('pvermees/provenance')
```

See https://www.ucl.ac.uk/~ucfbpve/provenance/

This project is licensed under the GPL-3 License

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