Estimation of multivariate differences between two groups (e.g., multivariate sex differences) with regularized regression methods and predictive approach. See Ilmarinen et al. (2023) <doi:10.1177/08902070221088155>. Deconstructing difference score correlations (e.g., gender-equality paradox), see Ilmarinen & Lönnqvist (2024) <doi:10.1037/pspp0000508>.
    Includes also tools that help in understanding difference score reliability, conditional intra-class correlations, tail-dependency, and heterogeneity of variance estimates. Package development was supported by the Academy of Finland research grant 338891.

Performs multi-omic differential network 
      analysis by revealing differential interactions between molecular entities
      (genes, proteins, transcription factors, or other biomolecules) across the
      omic datasets provided. 
      For each omic dataset, a differential network is constructed where
      links represent statistically significant differential interactions between 
      entities. These networks are then integrated into a comprehensive visualization
      using distinct colors to distinguish interactions from different omic layers.
      This unified display allows interactive exploration of cross-omic
      patterns, such as differential interactions present at both transcript and
      protein levels. For each link, users can access differential statistical 
      significance metrics (p values or adjusted p values, calculated via robust or 
      traditional linear regression with interaction term) and differential regression
      plots.  
      The methods implemented in this package are described in Sciacca et al. (2023)
      <doi:10.1093/bioinformatics/btad192>.

Deploy file changes across multiple 'GitHub'
    repositories using the 'GitHub' 'Web API' <https://docs.github.com/en/rest>.
    Allows synchronizing common files, Continuous Integration ('CI') workflows,
    or configurations across many repositories with a single command.

Code to support a systems biology research program from inception through publication.  The methods focus on dimension reduction approaches to detect patterns in complex, multivariate experimental data and places an emphasis on informative visualizations.  The goal for this project is to create a package that will evolve over time, thereby remaining relevant and reflective of current methods and techniques.  As a result, we encourage suggested additions to the package, both methodological and graphical.