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.

Likelihood-based and Bayesian methods for various compound Poisson linear models based on Zhang, Yanwei (2013) <doi:10.1007/s11222-012-9343-7>.

Provides functions to produce some circular plots for circular data, in a height- or area-proportional manner. They include bar plots, smooth density plots, stacked dot plots, histograms, multi-class stacked smooth density plots, and multi-class stacked histograms.

Sequential and batch change detection for univariate data streams, using the change point model framework. Functions are provided to allow nonparametric distribution-free change detection in the mean, variance, or general distribution of a given sequence of observations. Parametric change detection methods are also provided for Gaussian, Bernoulli and Exponential sequences. Both the batch (Phase I) and sequential (Phase II) settings are supported, and the sequences may contain either a single or multiple change points. A full description of this package is available in Ross, G.J (2015) - "Parametric and nonparametric sequential change detection in R" available at <https://www.jstatsoft.org/article/view/v066i03>.