Download geyser eruption and observation data from the GeyserTimes site (<https://geysertimes.org>) and optionally store it locally. The vignette shows a simple analysis of downloading, accessing, and summarizing the data.

This package provides a variety of functions to analyze and model geostatistical count data with Gaussian copulas, including 1) data simulation and visualization; 2) correlation structure assessment (here also known as the Normal To Anything); 3) calculate multivariate normal rectangle probabilities; 4) likelihood inference and parallel prediction at predictive locations. Description of the method is available from: Han and DeOliveira (2018) <doi:10.18637/jss.v087.i13>.

Uses a slice sampling-based Markov chain Monte Carlo to conduct Bayesian fitting and inference for generalized additive mixed models. Generalized linear mixed models and generalized additive models are also handled as special cases of generalized additive mixed models. The methodology and software is described in Pham, T.H. and Wand, M.P. (2018). Australian and New Zealand Journal of Statistics, 60, 279-330 <DOI:10.1111/ANZS.12241>.

Fit penalized multivariable linear mixed models with a single random effect to control for population structure in genetic association studies. The goal is to simultaneously fit many genetic variants at the same time, in order to select markers that are independently associated with the response. Can also handle prior annotation information, for example, rare variants, in the form of variable weights. For more information, see the website below and the accompanying paper: Bhatnagar et al., "Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models", 2020, <DOI:10.1371/journal.pgen.1008766>.

This package provides a collection of layers for ggplot2'. Provides geoms built on linear and radial gradients from the grid package, giving areas, bars, paths, rectangles, and ridgelines a fading or glowing visual effect. Also includes mathematically driven layers â catenary curves, Chaikin's corner-cutting smoothing (Chaikin, 1974, <doi:10.1016/0146-664X(74)90028-8>), and Fourier-series reconstruction â plus Lexis diagrams, isotype bar charts.

An EM algorithm, Karl et al. (2013) <doi:10.1016/j.csda.2012.10.004>, is used to estimate the generalized, variable, and complete persistence models, Mariano et al. (2010) <doi:10.3102/1076998609346967>. These are multiple-membership linear mixed models with teachers modeled as "G-side" effects and students modeled with either "G-side" or "R-side" effects.

The Global Biodiversity Information Facility ('GBIF', <https://www.gbif.org>) sources data from an international network of data providers, known as nodes'. Several of these nodes - the "living atlases" (<https://living-atlases.gbif.org>) - maintain their own web services using software originally developed by the Atlas of Living Australia ('ALA', <https://www.ala.org.au>). galah enables the R community to directly access data and resources hosted by GBIF and its partner nodes.

This is a companion to Henry-Stewart talk by Zhao (2026, <doi:10.69645/FRFQ9519>), which gathers information, metadata and scripts to showcase modern genetic analysis -- ranging from testing of polymorphic variant(s) for Hardy-Weinberg equilibrium, association with traits using genetic and statistical models, Bayesian implementation, power calculation in study design, and genetic annotation. It also covers R integration with the Linux environment, GitHub, package creation and web applications. The earlier version by Zhao (2009, <doi:10.69645/DCRY5578>) provides a brief introduction to these topics.

This package provides two new layer types for displaying image data as layers within the Grammar of Graphics framework. Displays images using either a rectangle interface, with a fixed bounding box, or a point interface using a central point and general size parameter. Images can be given as local JPEG or PNG files, external resources, or as a list column containing raster image data.

This package provides tools for creating publication-ready dimensionality reduction plots, including Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). This package helps visualize high-dimensional data with options for custom labels, density plots, and faceting, using the ggplot2 framework Wickham (2016) <doi:10.1007/978-3-319-24277-4>.

Identifies implausible anthropometric (e.g., height, weight) measurements in irregularly spaced longitudinal datasets, such as those from electronic health records.

This package provides a simple and intuitive high-level language for music representation. Generates and embeds music scores and audio files in RStudio', R Markdown documents, and R Jupyter Notebooks'. Internally, uses MusicXML <https://github.com/w3c/musicxml> to represent music, and MuseScore <https://musescore.org/> to convert MusicXML'.

Access and analyze multi-band greenspace seasonality data cubes (available for 1,028 major global cities), global Normalized Difference Vegetation Index / land cover data from the European Space Agency WorldCover 10m Dataset, and Sentinel-2-l2a images. Users can download data using bounding boxes, city names, and filter by year or seasonal time window. The package also supports calculating human exposure to greenspace using a population-weighted greenspace exposure model introduced by Chen et al. (2022) <doi:10.1038/s41467-022-32258-4> based on Global Human Settlement Layer population data, and calculating a set of greenspace morphology metrics at patch and landscape levels.

This package provides a function that generates a customized correlation matrix based on limit values and proportions for intervals composed by its limits. It can also generate random matrices with low, medium, and high correlations, in which low, medium, and high thresholds are user-defined.

Estimates within and between time point interactions in experience sampling data, using the Graphical vector autoregression model in combination with regularization. See also Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.

The standard linear regression theory whether frequentist or Bayesian is based on an assumed (revealed?) truth (John Tukey) attitude to models. This is reflected in the language of statistical inference which involves a concept of truth, for example confidence intervals, hypothesis testing and consistency. The motivation behind this package was to remove the word true from the theory and practice of linear regression and to replace it by approximation. The approximations considered are the least squares approximations. An approximation is called valid if it contains no irrelevant covariates. This is operationalized using the concept of a Gaussian P-value which is the probability that pure Gaussian noise is better in term of least squares than the covariate. The precise definition given in the paper "An Approximation Based Theory of Linear Regression". Only four simple equations are required. Moreover the Gaussian P-values can be simply derived from standard F P-values. Furthermore they are exact and valid whatever the data in contrast F P-values are only valid for specially designed simulations. A valid approximation is one where all the Gaussian P-values are less than a threshold p0 specified by the statistician, in this package with the default value 0.01. This approximations approach is not only much simpler it is overwhelmingly better than the standard model based approach. The will be demonstrated using high dimensional regression and vector autoregression real data sets. The goal is to find valid approximations. The search function is f1st which is a greedy forward selection procedure which results in either just one or no approximations which may however not be valid. If the size is less than than a threshold with default value 21 then an all subset procedure is called which returns the best valid subset. A good default start is f1st(y,x,kmn=15) The best function for returning multiple approximations is f3st which repeatedly calls f1st. For more information see the papers: L. Davies and L. Duembgen, "Covariate Selection Based on a Model-free Approach to Linear Regression with Exact Probabilities", <doi:10.48550/arXiv.2202.01553>, L. Davies, "An Approximation Based Theory of Linear Regression", 2024, <doi:10.48550/arXiv.2402.09858>.

Summarises a collection of partitions into a single optimal partition. The objective function is the expected posterior loss, and the minimisation is performed through a greedy algorithm described in Rastelli, R. and Friel, N. (2017) "Optimal Bayesian estimators for latent variable cluster models" <DOI:10.1007/s11222-017-9786-y>.

To provide a comprehensive analysis of high dimensional longitudinal data,this package provides analysis for any combination of 1) simultaneous variable selection and estimation, 2) mean regression or quantile regression for heterogeneous data, 3) cross-sectional or longitudinal data, 4) balanced or imbalanced data, 5) moderate, high or even ultra-high dimensional data, via computationally efficient implementations of penalized generalized estimating equations.

This package provides tools for working with polygons with holes in ggplot2', with a new geom for drawing a polypath applying the evenodd or winding rules.

This package provides a simple wrapper for Wikipedia data. Specifically, this package looks to fill a gap in retrieving text data in a tidy format that can be used for Natural Language Processing.

This package implements a variant of the Self-Organizing Map (SOM) algorithm designed for mixed-attribute datasets. Similarity between observations is computed using the Gower distance, and categorical prototypes are updated via heuristic strategies (weighted mode and multinomial sampling). Provides functions for model fitting, mapping, visualization (U-Matrix and component planes), and evaluation, making SOM applicable to heterogeneous real-world data. For methodological details see Sáez and Salas (2026) <doi:10.1007/s41060-025-00941-6>.

The official implementation of the Global Livestock Environmental Assessment Model (GLEAM) of the Food and Agriculture Organization of the United Nations (FAO) in R. GLEAM-X provides a modular, transparent framework for simulating livestock production systems and quantifying their environmental impacts. Methodological background: MacLeod et al. (2017) "Invited review: A position on the Global Livestock Environmental Assessment Model (GLEAM)" <doi:10.1017/S1751731117001847>. Further information: <https://www.fao.org/gleam/en/>.

Density function and generation of random variables from the Generalized Inverse Normal (GIN) distribution from Robert (1991) <doi:10.1016/0167-7152(91)90174-P>. Also provides density functions and generation from the GIN distribution truncated to positive or negative reals. Theoretical guarantees supporting the sampling algorithms and an application to Bayesian estimation of network formation models can be found in the working paper Ding, Estrada and Montoya-Blandón (2023) <https://www.smontoyablandon.com/publication/networks/network_externalities.pdf>.

This package provides a procedure that uses target-decoy competition (or knockoffs) to reject multiple hypotheses in the presence of group structure. The procedure controls the false discovery rate (FDR) at a user-specified threshold.