The distributed online expectation maximization algorithms are used to solve parameters of Poisson mixture models. The philosophy of the package is described in Guo, G. (2022) <doi:10.1080/02664763.2022.2053949>.

The 'future' package provides a unifying parallelization framework for R that supports many parallel and distributed backends <doi:10.32614/RJ-2021-048>. The 'foreach' package provides a powerful API for iterating over an R expression in parallel. The 'doFuture' package brings the best of the two together. There are three ways to use this package. The recommended approach is to use 'y <- foreach(...) %do% { ... } |> futurize()' via the 'futurize' package (<doi:10.48550/arXiv.2601.17578>). The second alternative, which was formerly recommended, is to use 'y <- foreach(...) %dofuture% { ... }', which does not require using 'registerDoFuture()' and has many advantages over '%dopar%'. The third alternative is the traditional 'foreach' approach by registering the 'foreach' adapter 'registerDoFuture()' and so that 'y <- foreach(...) %dopar% { ... }' parallelizes with the 'future' framework.

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>.