MSmix：一个基于Spearman距离的Mallows模型混合模型对部分排序进行聚类的R包
MSmix: An R Package for clustering partial rankings via mixtures of Mallows Models with Spearman distance
AI总结：
本研究推出R包MSmix，通过带数据增强的EM算法实现带Spearman距离的Mallows混合模型的最大似然估计，支持任意部分排序与大量项目序列，提供不确定性量化功能，经模拟和真实数据集验证其实用性与计算性能优于同类软件。
摘要：
MSmix是一款最新开发的R包，用于实现带Spearman距离的有限Mallows模型混合模型的最大似然估计，可处理完整排序和部分排序数据。该包旨在实现计算上可行的模型参数估计流程，能够处理任意形式的部分排序以及包含大量项目的序列。频率学派的估计任务通过EM算法完成，算法整合了数据增强策略，以还原未观测到的异质性和缺失的秩次。该包还提供参数估计的不确定性量化功能，支持多种bootstrap方法和渐近置信区间。包内构建了适用于S3类对象的通用方法，可更高效地管理主流程的输出结果。研究通过在模拟数据集和真实原始排序数据集上的应用，验证了该包的实用性，以及其与同类竞争软件相比的计算性能。
英文摘要：
MSmix is a recently developed R package implementing maximum likelihood estimation of finite mixtures of Mallows models with Spearman distance for full and partial rankings. The package is designed to implement computationally tractable estimation routines of the model parameters, with the ability to handle arbitrary forms of partial rankings and sequences of a large number of items. The frequentist estimation task is accomplished via EM algorithms, integrating data augmentation strategies to recover the unobserved heterogeneity and the missing ranks. The package also provides functionalities for uncertainty quantification of the estimated parameters, via diverse bootstrap methods and asymptotic confidence intervals. Generic methods for S3 class objects are constructed for more effectively managing the output of the main routines. The usefulness of the package and its computational performance compared with competing software is illustrated via applications to both simulated and original real ranking datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
