An implementation of the RuleFit algorithm as described in
    Friedman & Popescu (2008) <doi:10.1214/07-AOAS148>. eXtreme Gradient
    Boosting ('XGBoost') is used to build rules, and 'glmnet' is used to
    fit a sparse linear model on the raw and rule features. The result is
    a model that learns similarly to a tree ensemble, while often offering
    improved interpretability and achieving improved scoring runtime in
    live applications. Several algorithms for reducing rule complexity are
    provided, most notably hyperrectangle de-overlapping. All algorithms
    scale to several million rows and support sparse representations to
    handle tens of thousands of dimensions.

Contains functions to identify tree-ring borders based on X-ray micro-density profiles and a Graphical User Interface (GUI) to visualize density profiles and correct tree-ring borders. Campelo F, Mayer K, Grabner M. (2019) <doi:10.1016/j.dendro.2018.11.002>.

Fits hierarchical regularized regression models
    to incorporate potentially informative external data, Weaver and Lewinger (2019) <doi:10.21105/joss.01761>. 
    Utilizes coordinate descent to efficiently fit regularized regression models both with and without
    external information with the most common penalties used in practice (i.e. ridge, lasso, elastic net). 
    Support for standard R matrices, sparse matrices and big.matrix objects.

This is a set of statistical quality control functions,  that allows plotting control charts and its iterations, process capability for variable and attribute control, highlighting the xrs_gr() function, like a first iteration for variable chart, meanwhile the we_rules() function detects non random patterns in sample.

An extension for the 'xml2' package to transform XML documents
    by applying an 'xslt' style-sheet.

Tools for estimating HIV incidence using cross-sectional recency testing data, adjusting for internal and external target populations and supporting subtype-specific parameters. The statistical methodology implemented builds on the framework described in Wang, Duerr, and Gao(2025) <doi:10.1002/sim.70216>.