An easy way to analyze international large-scale assessments and surveys in education or any other dataset that includes replicated weights (Balanced Repeated Replication (BRR) weights, Jackknife replicate weights,...) while also allowing for analysis with multiply imputed variables (plausible values). It supports the estimation of univariate statistics (e.g. mean, variance, standard deviation, quantiles), frequencies, correlation, linear regression and 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) <doi:10.48550/arXiv.1306.0237>.

Performs species distribution modeling for rare species with unprecedented accuracy (Mondanaro et al., 2023 <doi:10.1111/2041-210X.14066>) and finds the area of origin of species and past contact between them taking climatic variability in full consideration (Mondanaro et al., 2025 <doi:10.1111/2041-210X.14478>).

Testing and inference for regression models using residual randomization methods. The basis of inference is an invariance assumption on the regression errors, e.g., clustered errors, or doubly-clustered errors.