Plot an 'R' package's recursive dependency graph and tabulate the 
    number of unique downstream dependencies added by top-level dependencies. 
    This helps 'R' package developers identify which of their declared 
    dependencies add the most downstream dependencies in order to prioritize
    them for removal if needed. Uses graph stress minimization adapted from 
    Schoch (2023) <doi:10.21105/joss.05238> and originally reported in Gansner 
    et al. (2004) <doi:10.1007/978-3-540-31843-9_25>.

Import foreign statistical formats into R via the embedded
    'ReadStat' C library, <https://github.com/WizardMac/ReadStat>.

Provides a 'shiny'-based platform for sub-national monitoring of global development
     indicators using large-scale household surveys. The package supports the Demographic and
     Health Surveys, Multiple Indicator Cluster Surveys, Malaria Indicator Surveys, Integrated
     Household Budget Surveys, Service Provision Assessment surveys, and Living Standards
     Measurement Study surveys. It provides workflows for descriptive, diagnostic, predictive, and
     prescriptive spatial analytics, including a spatial equalizer for survey-shapefile
     integration, exploratory spatial data analysis, area-level small area estimation, spatial
     autoregressive and spatial error models, hierarchical multilevel models, spatial inequality
     metrics, spatial and temporal decomposition, and publication-ready reporting. The implemented
     methods are described in <doi:10.1111/j.1538-4632.1995.tb00338.x>,
     <doi:10.1007/s11749-018-0599-x>, and the reference identified by <isbn:9781118735787>. The
     software has been cited in applied geographic and multilevel health studies, including studies
     of arthritis resource allocation <doi:10.1016/j.jorep.2026.101000> and childhood stunting
     priorities <doi:10.1016/j.nutos.2026.100660>.

The package allows to simulate Hawkes process both in univariate and multivariate settings. It gives functions to compute different moments of the number of jumps of the process on a given interval, such as mean, variance or autocorrelation of process jumps on time intervals separated by a lag.