Assists for presentation and visualization of data from the Norwegian Health Quality Registries following the standardization based on the requirement specified by the National Service for Health Quality Registries. This requirement can be accessed from (<https://www.kvalitetsregistre.no/resultater-til-publisering-pa-nett>). Unfortunately the website is only available in Norwegian.

An R and Repast integration tool for running individual-based
    (IbM) simulation models developed using 'Repast Simphony' Agent-Based framework
    directly from R code supporting multicore execution. This package 
    integrates 'Repast Simphony' models within R environment, making easier 
    the tasks of running and analyzing model output data for 
    automated parameter calibration and for carrying out uncertainty and
    sensitivity analysis using the power of R environment.

A fast way to analyze International Large-Scale Assessments (ILSAs) or any other dataset 
  that includes replicated weights (Balanced Repeated Replication (BRR) weights, Jackknife replicate weights,...) and/or plausible values.
  'Rrepest' contains functionalities that enable you to calculate basic statistics (means, correlations, etc.),
  frequencies, linear regression, or any other model already implemented in R that takes a data frame and weights
  as parameters. It also includes options to prepare the results for publication, following the table formatting standards of the 
  Organization for Economic Cooperation and Development (OECD).

Functions for studying realized genetic relatedness between people. Users will be able to simulate inheritance patterns given pedigree structures, generate SNP marker data given inheritance patterns, and estimate realized relatedness between pairs of individuals using SNP marker data. See Wang (2017) <doi:10.1534/genetics.116.197004>. This work was supported by National Institutes of Health grants R37 GM-046255.

Feature Selection with Regularized Random Forest. This
    package is based on the 'randomForest' package by Andy Liaw.
    The key difference is the RRF() function that builds a
    regularized random forest. Fortran original by Leo Breiman 
    and Adele Cutler, R port by Andy Liaw and Matthew Wiener, 
    Regularized random forest for classification by Houtao Deng, 
    Regularized random forest for regression by Xin Guan.
    Reference: Houtao Deng (2013) <arXiv:1306.0237>.