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>.

Rare variant association test integrating variant position information. It aims to identify the presence of clusters of disease-risk variants in specific gene regions. For more details, please read the publication from Persyn et al. (2017)  <doi:10.1371/journal.pone.0179364>.

The 'future' package provides a unifying parallelization framework for R that supports many parallel and distributed backends. 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 two alternative ways to use this package. The recommended approach is to use 'y <- foreach(...) %dofuture% { ... }', which does not require using 'registerDoFuture()' and has many advantages over '%dopar%'. The alternative is the traditional 'foreach' approach by registering the 'foreach' adapter 'registerDoFuture()' and so that 'y <- foreach(...) %dopar% { ... }' runs in 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) <arXiv:2209.07334>.

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

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.