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.

Create HTML5 slides with R Markdown and the JavaScript library remark.js (<https://remarkjs.com>).

Grammatical evolution (see O'Neil, M. and Ryan, C. (2003,ISBN:1-4020-7444-1)) uses decoders to convert linear (binary or integer genes) into programs. In addition, automatic determination of codon precision with a limited rule choice bias is provided. For a recent survey of grammatical evolution, see Ryan, C., O'Neill, M., and Collins, J. J. (2018) <doi:10.1007/978-3-319-78717-6>.

Allows to provide live interpretations and explanations of statistical functions in R. These interpretations and explanations are shown when the explained function is called by the user. They can interact with the values of the explained function's actual results to offer relevant, meaningful insights. The xplain interpretations and explanations are based on an easy-to-use XML format that allows to include R code to interact with the returns of the explained function.

This package provides 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>.

An R interface to the OpenPyXL Python library to create native Excel charts and work with Microsoft Excel files.

This package provides tools to analyze sex differences in omics data for complex diseases. It includes functions for differential expression analysis using the limma method <doi:10.1093/nar/gkv007>, interaction testing between sex and disease, pathway enrichment with clusterProfiler <doi:10.1089/omi.2011.0118>, and gene regulatory network (GRN) construction and analysis using igraph'. The package enables a reproducible workflow from raw data processing to biological interpretation.

Read and write XES Files to create event log objects used by the bupaR framework. XES (Extensible Event Stream) is the `IEEE` standard for storing and sharing event data (see <http://standards.ieee.org/findstds/standard/1849-2016.html> for more info).

Draw images easily, set up a plot with an image, specify where that image should be placed. Image plot by default reflects the index of the image data itself, or can be specified in simple extent terms xmin,xmax,ymin,ymax'. Numeric matrices, integer arrays, byte arrays, character arrays, and native rasters are (or will be) supported. A combination of image() and rasterImage() from the graphics package with their good features in one place.

This package provides functions for Estimating a (c)DCC-GARCH Model in large dimensions based on a publication by Engle et,al (2017) <doi:10.1080/07350015.2017.1345683> and Nakagawa et,al (2018) <doi:10.3390/ijfs6020052>. This estimation method is consist of composite likelihood method by Pakel et al. (2014) <http://paneldataconference2015.ceu.hu/Program/Cavit-Pakel.pdf> and (Non-)linear shrinkage estimation of covariance matrices by Ledoit and Wolf (2004,2015,2016). (<doi:10.1016/S0047-259X(03)00096-4>, <doi:10.1214/12-AOS989>, <doi:10.1016/j.jmva.2015.04.006>).

This package implements the Cross-Sectionally Augmented Panel Quantile Autoregressive Distributed Lag (CS-PQARDL) model and the Quantile Common Correlated Effects Mean Group (QCCEMG) estimator for panel data with cross-sectional dependence. The package handles unobserved common factors through cross-sectional averages following Pesaran (2006) <doi:10.1111/j.1468-0262.2006.00692.x> and Chudik and Pesaran (2015) <doi:10.1016/j.jeconom.2015.03.007>. Quantile regression for dynamic panels follows Harding, Lamarche, and Pesaran (2018) <doi:10.1016/j.jeconom.2018.07.010>. The ARDL approach to cointegration testing is based on Pesaran, Shin, and Smith (2001) <doi:10.1002/jae.616>.

This package provides tools to analyze datasets previous to any statistical modeling. Has various functions designed to find inconsistencies and understanding the distribution of the data.

The x3p file format is specified in ISO standard 5436:2000 to describe 3d surface measurements. x3ptools allows reading, writing and basic modifications to the 3D surface measurements.

This package implements panel cointegration tests allowing for structural breaks and cross-section dependence following the methodology of Banerjee and Carrion-i-Silvestre (2015) <doi:10.1002/jae.2348>. The package provides iterative factor-break estimation, individual ADF tests on defactored residuals, standardized panel test statistics, and the Bai and Ng (2004) <doi:10.1111/j.1468-0262.2004.00528.x> MQ test for identifying common stochastic trends. Supports five model specifications with varying deterministic components and break structures.

Integrates concepts of stochastic demography into species distribution modelling. The main approach maximizes a likelihood function based on environmental information and presence/absence records. This is used to reconstruct species fundamental ecological niches and to project their potential geographic range. Data requirements include species presence/absence records and a timeseries of environmental data.

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.

XMRs combine X-Bar control charts and Moving Range control charts. These functions also will recalculate the reference lines when significant change has occurred.

Adding some at-present missing functionality, or functions unlikely to be added to the base xpose package. This includes some diagnostic plots that have been missing in translation from xpose4', but also some useful features that truly extend the capabilities of what can be done with xpose'. These extensions include the concept of a set of xpose objects, and diagnostics for likelihood-based models.

Datasets and definitions of generic functions used in dependencies of the xergm package.

Support for interfaces from R to other languages, built around a class for evaluators and a combination of functions, classes and methods for communication. Will be used through a specific language interface package. Described in the book "Extending R".

Extension to xpose to support nlmixr2'. Provides functions to import nlmixr2 fit data into an xpose data object, allowing the use of xpose for nlmixr2 model diagnostics.

Helpers for transforming XML content into number of tables while preserving parent to child relationships.

The xtdml package implements partially linear panel regression (PLPR) models with high-dimensional confounding variables and an exogenous treatment variable within the double machine learning framework. The package is used to estimate the structural parameter (treatment effect) in static panel data models with fixed effects using the approaches established in Clarke and Polselli (2025) <doi:10.1093/ectj/utaf011>. xtdml follows the object-oriented architecture of DoubleML (Bach et al., 2024) <doi:10.18637/jss.v108.i03> and uses the mlr3 ecosystem.

This package provides a fast and elegant interface for generating XML fragments and documents. It can be used in companion with R packages XML or xml2 to generate XML documents. The fast XML generation is implemented using the Rcpp package.