Is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest, adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept. Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites.

Filter CpGs based on Intra-class Correlation Coefficients (ICCs) when replicates are available. ICCs are calculated by fitting linear mixed effects models to all samples including the un-replicated samples. Including the large number of un-replicated samples improves ICC estimates dramatically. The method accommodates any replicate design.

Provides access to consolidated information from the Brazilian Federal Government Payment Card. Includes functions to retrieve, clean, and organize data directly from the Transparency Portal <https://portaldatransparencia.gov.br/download-de-dados/cpgf/> and a curated dataset hosted on the Open Science Framework <https://osf.io/z2mxc/>.
    Useful for public spending analysis, transparency research, and reproducible workflows in auditing or investigative journalism.

Functions to generate ensembles of generalized linear models using 
             competing proximal gradients. The optimal sparsity and diversity
             tuning parameters are selected via an alternating grid search.

A general test for conditional independence in supervised learning 
  algorithms as proposed by Watson & Wright (2021) <doi:10.1007/s10994-021-06030-6>. 
  Implements a conditional variable importance measure which can be applied to any supervised 
  learning algorithm and loss function. Provides statistical inference procedures without 
  parametric assumptions and applies equally well to continuous and categorical predictors 
  and outcomes.