Work with data on Venetian doges and dogaresse and the noble families of the Republic of Venice, and use it for social network analysis, as used in Merelo (2022) <doi:10.48550/arXiv.2209.07334>.

Distributed Online Goodness-of-Fit Test can process the distributed  datasets. The philosophy of the package is described in Guo G.(2024) <doi:10.1016/j.apm.2024.115709>.

Read 'Word' documents containing bibliographic references, search for corresponding DOIs using the 'Crossref' API, and append the retrieved DOIs directly to the references. Supports parallel processing for faster retrieval and produces a new 'Word' document with numbered references including DOIs.

Provides a parallel backend for the %dopar% function using
        the multicore functionality of the parallel package.

Distributed Online Mean Tests  is a powerful tool designed to efficiently process and analyze distributed datasets. It enables users to perform mean tests in an online, distributed manner, making it highly suitable for large-scale data analysis. By leveraging advanced computational techniques, 'Domean' ensures robust and scalable solutions for statistical analysis, particularly in scenarios where data is dispersed across multiple nodes or sources. This package is ideal for researchers and practitioners working with high-dimensional data, providing a flexible and efficient framework for mean  testing. The philosophy of 'Domean' is described in Guo G.(2025) <doi:10.1016/j.physa.2024.130308>.

Dominance analysis is a method that allows to compare the
             relative importance of predictors in multiple regression models:
             ordinary least squares, generalized linear models, 
             hierarchical linear models, beta regression and dynamic linear models. 
             The main principles and methods of 
             dominance analysis are described in
             Budescu, D. V. (1993) <doi:10.1037/0033-2909.114.3.542> and  
             Azen, R., & Budescu, D. V. (2003) <doi:10.1037/1082-989X.8.2.129>
             for ordinary least squares regression. Subsequently, the extensions 
             for multivariate regression, logistic regression and
             hierarchical linear models were described in 
             Azen, R., & Budescu, D. V. (2006) <doi:10.3102/10769986031002157>,
             Azen, R., & Traxel, N. (2009) <doi:10.3102/1076998609332754> and
             Luo, W., & Azen, R. (2013) <doi:10.3102/1076998612458319>,
             respectively.