Age estimation plays a significant role in forensic anthropology and bioarchaeology. However, widely-used traditional methods involving macroscopic observation suffer from subjectivity and statistical bias. The present research aims to minimize both issues by applying computational and mathematical approaches. A laser scanner was used to reconstruct 890 auricular surfaces of adult individuals from three known-age European skeletal collections. Dirichlet Normal Energy (DNE) was applied to assess the curvature of the auricular surface and its relationship with known age-at-death. Ten variables had high correlations, including total DNE per Total polygon faces, Mean value of DNE on apex, proportion of polygon faces with DNE of less than 0.0001 and proportion of polygon faces with DNE of over 0.6. The variables were used to develop age prediction models which are freely available in a novel R package, JSDNE. The package predicts age mathematically, objectively, and user-independently. It includes three functions: principal component quadratic discriminant analysis (PCQDA), principal component regression analysis (PCR), and principal component logistic regression analysis (PCLR), which produce age estimates with 91%, 76%, and 92.9% levels of accuracy, respectively. JSDNE package (https://cran.r-project.org/package=JSDNE) can be downloaded automatically using install.packages("JSDNE"). The detailed code and the raw data of this study are openly available at https://github.com/jisunjang19/cran-JSDNE, doi: 10.5281/zenodo.12708779 or ‘JSDNE’ package.

中文翻译：

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'JSDNE'：一种新型 R 包，用于通过狄利克雷法向能量定量估计耳表面的年龄


年龄估计在法医人类学和生物考古学中起着重要作用。然而，广泛使用的传统宏观观察方法存在主观性和统计偏差。本研究旨在通过应用计算和数学方法最大限度地减少这两个问题。使用激光扫描仪从三个已知年龄的欧洲骨骼标本中重建了 890 个成年个体的耳表面。应用狄利克雷正常能量 （DNE） 评估耳表面的曲率及其与已知死亡年龄的关系。10 个变量具有高度相关性，包括每个多边形总面的总 DNE、顶点上 DNE 的平均值、DNE 小于 0.0001 的多边形面的比例以及 DNE 大于 0.6 的多边形面的比例。这些变量用于开发年龄预测模型，这些模型可在新颖的 R 包 JSDNE 中免费获得。该软件包以数学方式、客观地、独立于用户地预测年龄。它包括三个功能：主成分二次判别分析 （PCQDA）、主成分回归分析 （PCR） 和主成分逻辑回归分析 （PCLR），它们分别以 91%、76% 和 92.9% 的准确率生成年龄估计。JSDNE 软件包 （https://cran.r-project.org/package=JSDNE） 可以使用 install.packages（“JSDNE”） 自动下载。本研究的详细代码和原始数据可在 https://github.com/jisunjang19/cran-JSDNE、doi： 10.5281/zenodo.12708779 或 'JSDNE' 包中公开获得。