# Quantitative Archaeology Wiki

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# Quantitative Archaeology Wiki

This wiki is dedicated to the development of free documentation about quantitative methods in archaeology, using free/open source software.

## Contents

### Digging Numbers

Our first attempt is to rewrite the exercises for the textbook “Digging Numbers” by Fletcher and Lock using the `R` statistical software. This part is still in development, so if you can help, please do it. ☛ Go to the Digging Numbers exercises.

### Sum of individual weighted means

This is a method to calculate with enhanced precision the chronological distribution of archaeological artefacts that have long time spans. ☛ Go to the weighted means page

### Contingency Tables

This is a basic introduction to frequency and contingency tables using `R`. A function for plotting Ford “battleship” diagrams is presented. ☛ Go to the contingency tables page

### Spatial Analysis

Tutorials about spatial analysis techniques using free geospatial software like GRASS, R and others. ☛ Go to the spatial analysis introduction page

### Archaeometry

Statistical analysis is very often already included in the archaeometric study of materials. Here we try to perform some common analysis with the R programming language. ☛ Go to the archaeometry page

### Monte Carlo simulation

Enrico R. Crema has some interesting R scripts for Monte Carlo simulation on his website: http://www.homepages.ucl.ac.uk/~tcrnerc/Downloads.html

### Quantitative Archaeology summer school

Some brief notes taken at the I-QMDAA Summer School in 2006.

## Bibliography

We are collecting references to published manuals and journals about Quantitative Archaeology in this section.

# Mailing List

There's an international mailing list to discuss about quantitative methods, free software in archaeology, open formats and more. The list is kindly hosted by the Italian Linux Society.

However, the Antiquist Google Group is more active than “our” mailing list and you may get better answers there.

This means that:

1. you can contribute and improve the existing documentation, even with small pieces
2. you should contribute documentation about free and open source software (but general documentation about algorithms and statistical methods is also good)
3. anyone will be able to read, edit and possibly improve your contributions