We use a Bayesian approach to run individual patient data meta-analysis and network meta-analysis using 'JAGS'. The methods incorporate shrinkage methods and calculate patient-specific treatment effects as described in Seo et al. (2021) <DOI:10.1002/sim.8859>. This package also includes user-friendly functions that impute missing data in an individual patient data using mice-related packages.

A modern view on the principal component analysis biplot with 
    calibrated axes. Create principal component analysis biplots rendered in 
    HTML with significant reactivity embedded within the plot. Furthermore, 
    the traditional biplot view is enhanced by translated axes with inter-class
    kernel densities superimposed. For more information on biplots, see 
    Gower, J.C., Lubbe, S. and le Roux, N.J. (2011, ISBN: 978-0-470-01255-0).

A GUI with which the user can construct and interact with Bootstrap methods on Classical Biplots and with Clustering and/or Disjoint Biplot. This GUI is also aimed for estimate any numerical data matrix using the Clustering and Disjoint Principal component (CDPCA) methodology.

Provides users with an EZ-to-use platform for representing data
             with biplots. Currently principal component analysis (PCA), canonical variate
             analysis (CVA) and simple correspondence analysis (CA) biplots are included. 
             This is accompanied by various formatting
             options for the samples and axes. Alpha-bags and concentration ellipses
             are included for visual enhancements and interpretation. For an extensive
             discussion on the topic, see Gower, J.C., Lubbe, S. and le Roux, N.J.
             (2011, ISBN: 978-0-470-01255-0) Understanding Biplots. Wiley: Chichester.

Provides a GUI with which users can construct and interact
        with biplots.

Highly efficient functions for estimating various rank (centrality) measures of nodes in bipartite graphs (two-mode networks). Includes methods for estimating HITS, CoHITS, BGRM, and BiRank with implementation primarily inspired by He et al. (2016) <doi:10.1109/TKDE.2016.2611584>. Also provides easy-to-use tools for efficiently estimating PageRank in one-mode graphs, incorporating or removing edge-weights during rank estimation, projecting two-mode graphs to one-mode, and for converting edgelists and matrices to sparseMatrix format. Best of all, the package's rank estimators can work directly with common formats of network data including edgelists (class data.frame, data.table, or tbl_df) and adjacency matrices (class matrix or dgCMatrix).