It enables detailed interpretation of complex classification and regression models through Shapley analysis including data-driven characterization of subgroups of individuals. Furthermore, it facilitates multi-measure model evaluation, model fairness, and decision curve analysis. Additionally, it offers enhanced visualizations with interactive elements.

Current layout algorithms such as Kamada Kawai do not take into consideration disjoint clusters in a network, often resulting in a high overlap among the clusters, resulting in a visual “hairball” that often is uninterpretable. The ExplodeLayout algorithm takes as input (1) an edge list of a unipartite or bipartite network, (2) node layout coordinates (x, y) generated by a layout algorithm such as Kamada Kawai, (3) node cluster membership generated from a clustering algorithm such as modularity maximization, and (4) a radius to enable the node clusters to be “exploded” to reduce their overlap. The algorithm uses these inputs to generate new layout coordinates of the nodes which “explodes” the clusters apart, such that the edge lengths within the clusters are preserved, while the edge lengths between clusters are recalculated. The modified network layout with nodes and edges are displayed in two dimensions. The user can experiment with different explode radii to generate a layout which has sufficient separation of clusters, while reducing the overall layout size of the network. This package is a basic version of an earlier version called [epl]<https://github.com/UTMB-DIVA-Lab/epl> that searched for an optimal explode radius, and offered multiple ways to separate clusters in a network (Bhavnani et al(2017) <https://pmc.ncbi.nlm.nih.gov/articles/PMC5543384/>). The example dataset is for a bipartite network, but the algorithm can work also for unipartite networks.

Shiny interfaces and graphical functions for multivariate analysis results exploration.

Conduct numerous exploratory analyses in an instant with a 
    point-and-click interface. With one simple command, this tool 
    launches a Shiny App on the local machine. Drag and drop variables 
    in a data set to categorize them as possible independent, 
    dependent, moderating, or mediating variables. Then run dozens 
    (or hundreds) of analyses instantly to uncover any statistically 
    significant relationships among variables. Any relationship 
    thus uncovered should be tested in follow-up studies. 
    This tool is designed only to facilitate exploratory 
    analyses and should NEVER be used for p-hacking. Many of 
    the functions used in this package are previous versions of functions
    in the R Packages 'kim' and 'ezr'.
    Selected References:
    Chang et al. (2021) <https://CRAN.R-project.org/package=shiny>.
    Dowle et al. (2021) <https://CRAN.R-project.org/package=data.table>.
    Kim (2023) <https://jinkim.science/docs/kim.pdf>.
    Kim (2021) <doi:10.5281/zenodo.4619237>.
    Kim (2020) <https://CRAN.R-project.org/package=ezr>.
    Simmons et al. (2011) <doi:10.1177/0956797611417632>
    Tingley et al. (2019) <https://CRAN.R-project.org/package=mediation>.
    Wickham et al. (2020) <https://CRAN.R-project.org/package=ggplot2>.