Cosine Quantogram Analysis (CQA) is a statistical analysis employed in archaeology for the study of numerical datasets with hypothesized quantal distribution. To verify thesignificance of the results, the analysis is often combined with the execution of Monte Carlo simulations. In this article, we present a freely downloadable Python package (CQArchaeo) that integrates CQA and Monte Carlo simulations in the same environment, making the analysis customizable in the main parameters. We provide a guide that enables the use of this tool even for researchers with limited experience in Python programming and demonstrate the applicability, functioning, and main limitations of the analysis on some archaeological datasets.

Giancarlo Lago, Lorenzo Cardarelli, Nicola Ialongo (2024). CQArchaeo: a Python package for Cosine Quantogram Analysis and Monte Carlo simulations. ARCHEOLOGIA E CALCOLATORI. SUPPLEMENTO, 35(1), 215-232 [10.19282/ac.35.1.2024.15].

CQArchaeo: a Python package for Cosine Quantogram Analysis and Monte Carlo simulations

Giancarlo Lago
;
2024

Abstract

Cosine Quantogram Analysis (CQA) is a statistical analysis employed in archaeology for the study of numerical datasets with hypothesized quantal distribution. To verify thesignificance of the results, the analysis is often combined with the execution of Monte Carlo simulations. In this article, we present a freely downloadable Python package (CQArchaeo) that integrates CQA and Monte Carlo simulations in the same environment, making the analysis customizable in the main parameters. We provide a guide that enables the use of this tool even for researchers with limited experience in Python programming and demonstrate the applicability, functioning, and main limitations of the analysis on some archaeological datasets.
2024
Giancarlo Lago, Lorenzo Cardarelli, Nicola Ialongo (2024). CQArchaeo: a Python package for Cosine Quantogram Analysis and Monte Carlo simulations. ARCHEOLOGIA E CALCOLATORI. SUPPLEMENTO, 35(1), 215-232 [10.19282/ac.35.1.2024.15].
Giancarlo Lago; Lorenzo Cardarelli; Nicola Ialongo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/982400
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