Allows users to quickly and easily generate fake data containing Personally Identifiable Information (PII) through convenience functions.

This package provides a ggplot2 extension that enables visualization of IP (Internet Protocol) addresses and networks. The address space is mapped onto the Cartesian coordinate system using a space-filling curve. Offers full support for both IPv4 and IPv6 (Internet Protocol versions 4 and 6) address spaces.

Inspects ggplot objects by extracting their full declarative specification - layers, aesthetic mappings, scales, facets, coordinate systems, and labels - as tidy data frames. A second tier of functions enables structural comparison of two ggplot objects, supporting automated plot testing, auditing, and framework-agnostic grading workflows. Unlike ggcheck', which is designed exclusively for learnr'/'gradethis pipelines and returns ad-hoc objects, ggspec returns rectangular, pipeable output and does not require any grading framework as a dependency.

This package provides a fully automated workflow for calibrating and analyzing light-level geolocation ('GLS') data from seabirds and other wildlife. The glscalibrator package auto-discovers birds from directory structures, automatically detects calibration periods from the first days of deployment, processes multiple individuals in batch mode, and generates standardized outputs including position estimates, diagnostic plots, and quality control metrics. Implements the established threshold workflow internally, following the methods described in SGAT (Wotherspoon et al. (2016) <https://github.com/SWotherspoon/SGAT>), GeoLight (Lisovski et al. (2012) <doi:10.1111/j.2041-210X.2012.00185.x>), and TwGeos (Lisovski et al. (2019) <https://github.com/slisovski/TwGeos>).

Extends classical linear and quadratic discriminant analysis by incorporating permutation group symmetries into covariance matrix estimation. The package leverages methodology from the gips framework to identify and impose permutation structures that act as a form of regularization, improving stability and interpretability in settings with symmetric or exchangeable features. Several discriminant analysis variants are provided, including pooled and class-specific covariance models, as well as multi-class extensions with shared or independent symmetry structures. For more details about gips methodology see and Graczyk et al. (2022) <doi:10.1214/22-AOS2174> and Chojecki, Morgen, KoÅ odziejek (2025, <doi:10.18637/jss.v112.i07>).

An extension of ggplot2 to provide quiver plots to visualise vector fields. This functionality is implemented using a geom to produce a new graphical layer, which allows aesthetic options. This layer can be overlaid on a map to improve visualisation of mapped data.

Description: For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that gene-environment interactions play important roles beyond the main genetic and environmental effects. In practical interaction analyses, outliers in response variables and covariates are not uncommon. In addition, missingness in environmental factors is routinely encountered in epidemiological studies. The developed package consists of five robust approaches to address the outliers problems, among which two approaches can also accommodate missingness in environmental factors. Both continuous and right censored responses are considered. The proposed approaches are based on penalization and sparse boosting techniques for identifying important interactions, which are realized using efficient algorithms. Beyond the gene-environment analysis, the developed package can also be adopted to conduct analysis on interactions between other types of low-dimensional and high-dimensional data. (Mengyun Wu et al (2017), <doi:10.1080/00949655.2018.1523411>; Mengyun Wu et al (2017), <doi:10.1002/gepi.22055>; Yaqing Xu et al (2018), <doi:10.1080/00949655.2018.1523411>; Yaqing Xu et al (2019), <doi:10.1016/j.ygeno.2018.07.006>; Mengyun Wu et al (2021), <doi:10.1093/bioinformatics/btab318>).

Penalised likelihood estimation of a covariance matrix via the ridge-regularised covglasso estimator described in Cibinel et al. (2024) <doi:10.48550/arXiv.2410.02403>. Based on the C++ code of the R package covglasso (by Michael Fop, <https://orcid.org/0000-0003-3936-2757>) and the R code of icf (by Mathias Drton, <https://orcid.org/0000-0001-5614-3025>) within the R package ggm'.

Provide estimation and data generation tools for a generalization of the transmuted distributions discussed in Shaw and Buckley (2007). See <doi:10.48550/arXiv.0901.0434> for more information.

GEE estimation of the parameters in mean structures with possible correlation between the outcomes. User-specified mean link and variance functions are allowed, along with observation weighting. The M in the name geeM is meant to emphasize the use of the Matrix package, which allows for an implementation based fully in R.

This package provides functions for drawing node-and-edge graphs that have been laid out by graphviz'. This provides an alternative rendering to that provided by the Rgraphviz package, with two main advantages: the rendering provided by gridGraphviz should be more similar to what graphviz itself would draw; and rendering with grid allows for post-hoc customisations using the named viewports and grobs that gridGraphviz produces.

This package provides a genetic algorithm for finding variable subsets in high dimensional data with high prediction performance. The genetic algorithm can use ordinary least squares (OLS) regression models or partial least squares (PLS) regression models to evaluate the prediction power of variable subsets. By supporting different cross-validation schemes, the user can fine-tune the tradeoff between speed and quality of the solution.

R-interface to C++ implementation of the rank/score permutation based GSEA test (Subramanian et al 2005 <doi: 10.1073/pnas.0506580102>).

This package provides an extension to ggplot2 (Wickham, 2016, <doi:10.1007/978-3-319-24277-4>) for creating two types of continuous confidence interval plots (Violin CI and Gradient CI plots), typically for the sample mean. These plots contain multiple user-defined confidence areas with varying colours, defined by the underlying t-distribution used to compute standard confidence intervals for the mean of the normal distribution when the variance is unknown. Two types of plots are available, a gradient plot with rectangular areas, and a violin plot where the shape (horizontal width) is defined by the probability density function of the t-distribution. These visualizations are studied in (Helske, Helske, Cooper, Ynnerman, and Besancon, 2021) <doi:10.1109/TVCG.2021.3073466>.

Allows you to write queries that combine SQL (Structured Query Language) data retrieval with visualization specifications in a single, composable syntax. The ggsql package binds directly with the ggsql Rust library and allows you to set up readers and writers and execute queries against it. The package also offers knitr and shiny integration allowing the user to use ggsql in both frameworks.

This package provides functions and analytics for GENEA-compatible accelerometer data into R objects. See topic GENEAread for an introduction to the package. See <https://activinsights.com/technology/geneactiv/> for more details on the GENEActiv device.

Data sets and scripts used in the book Generalized Additive Models: An Introduction with R', Wood (2006,2017) CRC.

Efficient algorithms for fitting regularization paths for linear or logistic regression models penalized by LEP.

The gasanalyzer R package offers methods for importing, preprocessing, and analyzing data related to photosynthetic characteristics (gas exchange, chlorophyll fluorescence and isotope ratios). It translates variable names into a standard format, and can recalculate derived, physiological quantities using imported or predefined equations. The package also allows users to assess the sensitivity of their results to different assumptions used in the calculations. See also Tholen (2024) <doi:10.1093/aobpla/plae035>.

Density, distribution function, quantile function and random generation for the bimodal skew symmetric normal distribution of Hassan and El-Bassiouni (2016) <doi:10.1080/03610926.2014.882950>.

We consider the ultrahigh-dimensional and error-prone data. Our goal aims to estimate the precision matrix and identify the graphical structure of the random variables with measurement error corrected. We further adopt the estimated precision matrix to the linear discriminant function to do classification for multi-label classes.

Fits generalized additive models for the location, scale and shape parameters of a generalized extreme value response distribution. The methodology is based on Rigby, R.A. and Stasinopoulos, D.M. (2005), <doi:10.1111/j.1467-9876.2005.00510.x> and implemented using functions from the gamlss package <doi:10.32614/CRAN.package.gamlss>.

The ggplot2 package provides a powerful set of tools for visualising and investigating data. The ggsoccer package provides a set of functions for elegantly displaying and exploring soccer event data with ggplot2'. Providing extensible layers and themes, it is designed to work smoothly with a variety of popular sports data providers.

Implemented are the Wald-type statistic, a permuted version thereof as well as the ANOVA-type statistic for general factorial designs, even with non-normal error terms and/or heteroscedastic variances, for crossed designs with an arbitrary number of factors and nested designs with up to three factors. Friedrich et al. (2017) <doi:10.18637/jss.v079.c01>.