This package provides ggplot2 geoms analogous to geom_col() and geom_bar() that allow for treemaps using treemapify nested within each bar segment. Also provides geometries for subgroup bordering and text annotation.

Geostatistical analysis including variogram-based, likelihood-based and Bayesian methods. Software companion for Diggle and Ribeiro (2007) <doi:10.1007/978-0-387-48536-2>.

This package provides tools to download data from geoBoundaries <https://www.geoboundaries.org/>. Several administration levels available. See Runfola, D. et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLOS ONE 15(4): 1-9. <doi:10.1371/journal.pone.0231866>.

Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean, see Pretis, Reade and Sucarrat (2018) <doi:10.18637/jss.v086.i03> for an overview of the package. In advanced use, the estimator and diagnostics tests can be fully user-specified, see Sucarrat (2021) <doi:10.32614/RJ-2021-024>.

This package provides tools.

This package provides ggplot2 functions to return the results of seasonal and trading day adjustment made by RJDemetra'. RJDemetra is an R interface around JDemetra+ (<https://github.com/jdemetra/jdemetra-app>), the seasonal adjustment software officially recommended to the members of the European Statistical System and the European System of Central Banks.

This package provides functions to help with creating sparklines in the style of Edward Tufte <https://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0001OR&topic_id=1> in ggplot2'. It computes ribbon geoms with the interquartile ranges and points and/or labels at the beginning, end, max, and min points.

Genomic biology is not limited to the confines of the canonical B-forming DNA duplex, but includes over ten different types of other secondary structures that are collectively termed non-B DNA structures. Of these non-B DNA structures, the G-quadruplexes are highly stable four-stranded structures that are recognized by distinct subsets of nuclear factors. This package provide functions for predicting intramolecular G quadruplexes. In addition, functions for predicting other intramolecular nonB DNA structures are included.

Routines for log-linear models of incomplete contingency tables, including some latent class models, via EM and Fisher scoring approaches. Allows bootstrapping. See Espeland and Hui (1987) <doi:10.2307/2531553> for general approach.

Allows user to choose/gate a region on the plot and returns points within it.

This package provides tools for fitting sparse generalised linear mixed models with l0 regularisation. Selects fixed and random effects under the hierarchy constraint that fixed effects must precede random effects. Uses coordinate descent and local search algorithms to rapidly deliver near-optimal estimates. Gaussian and binomial response families are currently supported. For more details see Thompson, Wand, and Wang (2025) <doi:10.48550/arXiv.2506.20425>.

Neural networks are applied to create a density value function which approximates density values for a data source. The trained neural network is analyzed for different levels. For each level metric subspaces with density values above a level are determined. The obtained set of metric subspaces and the trained neural network are assembled into a data model. A prerequisite is the definition of a data source, the generation of generative data and the calculation of density values. These tasks are executed using package ganGenerativeData <https://cran.r-project.org/package=ganGenerativeData>.

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 Gaussian Interval Plot (GIplot) is a pictorial representation of the mean and the standard deviation of a quantitative variable. It also flags potential outliers (together with their frequencies) that are c standard deviations away from the mean.

An interactive git user interface from the R command line. Intuitive tools to make commits, branches, remotes, and diffs an integrated part of R coding. Built on git2r, a system installation of git is not required and has default on-premises remote option.

This package contains the Gene ontology terms and skeleton for the reduced GO directed acyclic graph (DAG) for the organisms Rat and Mouse. The methods are explicitly discussed in the following article : Manjang et al (2020) <doi:10.1038/s41598-020-73326-3>.

Retrieve datasets from the Global Data Lab website <https://globaldatalab.org> directly into R data frames. Functions are provided to reference available options (indicators, levels, countries, regions) as well.

This package provides functions for downloading of geographic data for use in spatial analysis and mapping. The package facilitates access to climate, crops, elevation, land use, soil, species occurrence, accessibility, administrative boundaries and other data.

Fits high dimensional penalized generalized linear mixed models using the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm. The purpose of the package is to perform variable selection on both the fixed and random effects simultaneously for generalized linear mixed models. The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm is described in Rashid et al. (2020) <doi:10.1080/01621459.2019.1671197>. The techniques used in the minimization portion of the procedure (the M-step) are derived from the procedures of the ncvreg package (Breheny and Huang (2011) <doi:10.1214/10-AOAS388>) and grpreg package (Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>), with appropriate modifications to account for the estimation and penalization of the random effects. The ncvreg and grpreg packages also describe the MCP, SCAD, and LASSO penalties.

It provides functions to generate operating characteristics and to calculate Sequential Conditional Probability Ratio Tests(SCPRT) efficacy and futility boundary values along with sample/event size of Multi-Arm Multi-Stage(MAMS) trials for different outcomes. The package is based on Jianrong Wu, Yimei Li, Liang Zhu (2023) <doi:10.1002/sim.9682>, Jianrong Wu, Yimei Li (2023) "Group Sequential Multi-Arm Multi-Stage Survival Trial Design with Treatment Selection"(Manuscript accepted for publication) and Jianrong Wu, Yimei Li, Shengping Yang (2023) "Group Sequential Multi-Arm Multi-Stage Trial Design with Ordinal Endpoints"(In preparation).

Create Primavera-style interactive Gantt charts with Work Breakdown Structure (WBS) hierarchy and activities. Features include color-coded WBS items, indented labels, scrollable views for large projects, dynamic date formatting, and the ability to dim past activities. Built on top of plotly for interactive visualizations.

This package provides a comprehensive suite of functions and RStudio Add-ins leveraging the capabilities of open-source Large Language Models (LLMs) to support R developers. These functions offer a range of utilities, including text rewriting, translation, and general query capabilities. Additionally, the programming-focused functions provide assistance with debugging, translating, commenting, documenting, and unit testing code, as well as suggesting variable and function names, thereby streamlining the development process.

This package provides a variety of multivariable data summary statistics and constructions have been proposed, either to generalize univariable analogs or to exploit multivariable properties. Notable among these are the bivariate peelings surveyed by Green (1981, ISBN:978-0-471-28039-2), the bag-and-bolster plots proposed by Rousseeuw &al (1999) <doi:10.1080/00031305.1999.10474494>, and the minimum spanning trees used by Jolliffe (2002) <doi:10.1007/b98835> to represent high-dimensional relationships among data in a low-dimensional plot. Additionally, biplots of singular value--decomposed tabular data, such as from principal components analysis, make use of vectors, calibrated axes, and other representations of variable elements to complement point markers for case elements; see Gabriel (1971) <doi:10.1093/biomet/58.3.453> and Gower & Harding (1988) <doi:10.1093/biomet/75.3.445> for original proposals. Because they treat the abscissa and ordinate as commensurate or the data elements themselves as point masses or unit vectors, these multivariable tools can be thought of as belonging to geometric data analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and applications and Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0> for foundations. gggda extends Wickham's (2010) <doi:10.1198/jcgs.2009.07098> layered grammar of graphics with statistical transformation ("stat") and geometric construction ("geom") layers for many of these tools, as well as convenience coordinate systems to emphasize intrinsic geometry of the data.

This package provides a Chernoff face geom for ggplot2'. Maps multivariate data to human-like faces. Inspired by Chernoff (1973) <doi:10.1080/01621459.1973.10482434>.