ZooaRchGUI: Novel Implementations to the Statistical Package for Archaeologists in the R Programming Language
This is an abstract from the "Novel Statistical Techniques in Archaeology I (QUANTARCH I)" session, at the 84th annual meeting of the Society for American Archaeology.
The study of zooarchaeological data illuminates some of the most important and challenging questions in archaeology. Statistical and other quantitative methods are frequently employed to address these questions by evaluating hypotheses with empirical evidence. Such methodologies range from standard "statistical tests" to novel, non-traditional inferential techniques. Unfortunately, no single standard software package exists to encompass the extent of analytical tools used by zooarchaeologists and archaeologists. In 2016, we developed ZooaRchGUI, an open-source and easy-to-use comprehensive statistical software designed for archaeologists interested in using traditional and non-standard statistical methods. ZooaRchGUI is an R package available from the Comprehensive R Archive Network (CRAN). It enables archaeologists unfamiliar with R-coding to manipulate, visualize, and analyze their data by utilizing the latest quantitative techniques available in the R programming environment via a user-friendly Graphical User Interface (GUI). We have made several advances to ZooaRchGUI. Newest developments make the underlying R code available to users, increasing the replicability and transparency of the results. This functionality also improves the utility of ZooaRchGUI as a teaching tool for R and quantitative methods more generally. We will continue to expand the functionality of ZooaRchGUI to provide the most complete experience for computational archaeology across all of its sub-disciplines.
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ZooaRchGUI: Novel Implementations to the Statistical Package for Archaeologists in the R Programming Language. John Rapes, Jesse Wolfhagen, Max Price, Erik Otárola-Castillo. Presented at The 84th Annual Meeting of the Society for American Archaeology, Albuquerque, NM. 2019 ( tDAR id: 451185)
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Abstract Id(s): 25295