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 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, 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.


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, 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..

Prerequisites

You must have R installed on your system (see http://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')

Installation

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')

Further information

See http://provenance.london-geochron.com

Vermeesch, P., Resentini, A. and Garzanti, E., 2016, An R package for statistical provenance analysis, Sedimentary Geology, 336, 14-25

Vermeesch, P., 2018, Statistical models for point-counting data. Earth and Planetary Science Letters (in press)

Author

Pieter Vermeesch

License

This project is licensed under the GPL-3 License

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Reference manual

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install.packages("provenance")

2.2 by Pieter Vermeesch, 2 months ago


http://provenance.london-geochron.com


Browse source code at https://github.com/cran/provenance


Authors: Pieter Vermeesch [aut, cre]


Documentation:   PDF Manual  


GPL-2 license


Imports MASS, methods, IsoplotR


See at CRAN