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.

Managing postgraduate programmes involves extracting information from Lattes CVs. This information can be used for strategic planning and self-evaluation, as well as for producing reports on the Sucupira Platform. Summary reports are produced for each period and course (specialisation, master's and doctorate), showing bibliographic production with and without student participation, as well as papers at events, technical or technological production, ongoing and completed supervision, research projects, exchanges (visiting professor, postdoctoral or short-term leave), awards and general activity indicators. Based on this information, a detailed report is then drawn up for each lecturer, taking into account their participation in exam boards, their research project contributions, their technical collaborations (e.g. advisory committee, editorial board) and the subjects they teach. For more details see Pagliosa and Nascimento (2021) <https://repositorio.ufsc.br/bitstream/handle/123456789/231602/ManualLattesGeociencias11_2021_versaobeta%20%281%29.pdf?sequence=1&isAllowed=y>.

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>.