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.

An implementation of representation-dependent gene level operations for genetic algorithms with genes representing permutations: Initialization of genes, mutation, and crossover. The crossover operation provided is position-based crossover (Syswerda, G., Chap. 21 in Davis, L. (1991, ISBN:0-442-00173-8). For mutation, several variants are included: Order-based mutation (Syswerda, G., Chap. 21 in Davis, L. (1991, ISBN:0-442-00173-8), randomized Lin-Kernighan heuristics (Croes, G. A. (1958) <doi:10.1287/opre.6.6.791> and Lin, S. and Kernighan. B. W. (1973) <doi:10.1287/opre.21.2.498>), and randomized greedy operators. A random mix operator for mutation selects a mutation variant randomly.

Supports a structured approach for exploring PKPD data <https://opensource.nibr.com/xgx/>. It also contains helper functions for enabling the modeler to follow best R practices (by appending the program name, figure name location, and draft status to each plot). In addition, it enables the modeler to follow best graphical practices (by providing a theme that reduces chart ink, and by providing time-scale, log-scale, and reverse-log-transform-scale functions for more readable axes). Finally, it provides some data checking and summarizing functions for rapidly exploring pharmacokinetics and pharmacodynamics (PKPD) datasets.

Implementation of Bayesian models for estimating object lengths and morphological relationships between object lengths using photographic data collected from drones. The Bayesian model is described in "Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from drones" (Bierlich et al., 2021, <doi:10.3354/meps13814>).

Converts an XLSForm (survey in Excel') into a well-structured Word document, including sections, skip logic, options, and question labels. Designed to support survey documentation, training materials, and data collection workflows. The package was developed based on field experience with XLSForm and humanitarian operations, aiming to streamline documentation and enhance training efficiency.

This package provides tools for interactive data exploration built using shiny'. Includes apps for descriptive statistics, visualizing probability distributions, inferential statistics, linear regression, logistic regression and RFM analysis.

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>.

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

Parse entire folders of non-rectangular xlsx files into a single rectangular and tidy data.frame based on a custom template file defining the column names of the output.

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.

Converts XML documents to R dataframes and dataframes to XML documents. A wide variety of options allows for different XML formats and flexible control of the conversion process. Results can be exported to CSV and Excel, if desired. Also converts XML data to R lists.

Download data from individual XKCD comics, written by Randall Munroe <https://xkcd.com/>.

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>).

Helps systematize and ease the process of building unit tests with the testthat package by providing tools for generating expectations.

This package provides a high-level interface for creating and exporting summary tables to Excel'. Built on dplyr and openxlsx', it provides tools for generating one-way to n-way tables, and summarizing multiple response questions and question blocks. Tables are exported with native Excel formatting, including titles, footnotes, and basic styling options.

This tool enables in-database scoring of XGBoost models built in R, by translating trained model objects into SQL query. XGBoost <https://github.com/dmlc/xgboost> provides parallel tree boosting (also known as gradient boosting machine, or GBM) algorithms in a highly efficient, flexible and portable way. GBM algorithm is introduced by Friedman (2001) <doi:10.1214/aos/1013203451>, and more details on XGBoost can be found in Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.

The XML-RPC is a remote procedure call protocol based on XML'. The xmlrpc2 package is inspired by the XMLRPC package but uses the curl and xml2 packages instead RCurl and XML'.

Miscellaneous functions used for x-engineering (feature engineering) or for supporting in other packages maintained by Shichen Xie'.

An implementation of the representation-dependent gene level operations of grammar-based genetic programming with genes which are derivation trees of a context-free grammar: Initialization of a gene with a complete random derivation tree, decoding of a derivation tree. Crossover is implemented by exchanging subtrees. Depth-bounds for the minimal and the maximal depth of the roots of the subtrees exchanged by crossover can be set. Mutation is implemented by replacing a subtree by a random subtree. The depth of the random subtree and the insertion node are configurable. For details, see Geyer-Schulz (1997, ISBN:978-3-7908-0830-X).

Create beautifully color-coordinated and customized themes for your xaringan slides, without writing any CSS. Complete your slide theme with ggplot2 themes that match the font and colors used in your slides. Customized styles can be created directly in your slides R Markdown source file or in a separate external script.

Extrema-weighted feature extraction for varying length functional data. Functional data analysis method that performs dimensionality reduction based on predefined features and allows for quantile weighting. Method implemented as presented in van den Boom et al. (2018) <doi:10.1093/bioinformatics/bty120>.

Representation-dependent gene-level operations for genetic and evolutionary algorithms with real-coded genes used in the R-package xega <https://CRAN.R-project.org/package=xega> are collected in this package. The common feature of the gene operations is that all of them are useful for derivation-free optimization algorithms. At the moment the package implements initialization, mutation, crossover, and replication operations for differential evolution as described in Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) <doi:10.1007/3-540-31306-0>. In addition, several (more recent) methods for determining the scale factor are provided. For xega''s architecture, see Geyer-Schulz, A. (2025) <doi:10.5445/IR/1000187255>.

Extras and extensions for xaringan slides. Navigate your slides with tile view. Make your slides editable, live! Announce slide changes with subtle tones. Animate slide transitions with animate.css'. Add tabbed panels to slides with panelset'. Use the Tachyons CSS utility toolkit for rapid slide development. Scribble on your slides. Add a copy button to your code chunks with clipboard'. Add a logo or top or bottom banner to every slide. Broadcast slides to stay in sync with remote viewers. Include yourself in your slides with webcam'. Plus a whole lot more!

Fit a two-step kernel ridge regression model for predicting edges in networks, and carry out cross-validation using shortcuts for swift and accurate performance assessment (Stock et al, 2018 <doi:10.1093/bib/bby095> ).