This package provides multiple palettes based on pride flags with tailored themes.

This package provides functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables. These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.

Generalized Order-Restricted Information Criterion (GORIC) value for a set of hypotheses in multivariate linear models and generalised linear models.

This package provides tools for decomposing Global Value Chain (GVC) participation and value-added trade. It implements the frameworks proposed by Borin and Mancini (2023) 10.1080/09535314.2022.2153221> for source-based and sink-based decompositions, and by Borin, Mancini, and Taglioni (2025) 10.1093/wber/lhaf017> for tripartite and output-based GVC measures.

Integrates game theory and ecological theory to construct social-ecological models that simulate the management of populations and stakeholder actions. These models build off of a previously developed management strategy evaluation (MSE) framework to simulate all aspects of management: population dynamics, manager observation of populations, manager decision making, and stakeholder responses to management decisions. The newly developed generalised management strategy evaluation (GMSE) framework uses genetic algorithms to mimic the decision-making process of managers and stakeholders under conditions of change, uncertainty, and conflict. Simulations can be run using gmse(), gmse_apply(), and gmse_gui() functions.

This package provides a series of aliases to commonly used but difficult to remember ggplot2 sequences.

This package provides a ggplot2 extension that provides functions for drawing chord diagrams for visualising flows between categories. The package extends ggplot2 by adding geoms and stats for drawing chord sectors, arcs, and labels.

This package provides a point-and-click shiny interface for the descriptive analysis that comes before any model is chosen. Pass a data frame, pick the variable to describe, and add the layers you want to see it within: a second variable becomes the panels of a ggplot2 facet_wrap(), and further variables become separate figures, one file each, taken either one variable at a time or crossed. Every stratum is reported with the number of observations behind it, on the figure and on each of its panels; strata that contain none are listed rather than dropped, and rows with a missing value in a layer variable are excluded and counted. A continuous variable can be categorized into quantile groups, equal-width bins or user-supplied cut points and then used as a layer; the figure types follow those offered by the ggplotgui package and add the line plot for change over time, an optional LOWESS smoother, and the Kaplan-Meier curve estimated by survival', with an optional number-at-risk table. Columns are described as they are typed, so convert each to the type you mean first. Figures are written as PNG or SVG, and the application prints the ggplot2 code behind the figure on screen, so that a description can be repeated, shared or accounted for later. Everything runs locally, with no network access and no AI involved.

This package provides methods to analyse experimental agriculture data, from data synthesis to model selection and visualisation. The package is named after W.S. Gosset aka â Studentâ , a pioneer of modern statistics in small sample experimental design and analysis.

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.

Fits gastric emptying time series from MRI or scintigraphic measurements using nonlinear mixed-model population fits with nlme and Bayesian methods with Stan; computes derived parameters such as t50 and AUC.

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.

Interacts with the Glassdoor API <https://www.glassdoor.com/developer/index.htm>. Allows the user to search job statistics, employer statistics, and job progression, where Glassdoor provides a breakdown of other jobs a person did after their current one.

Turns a single ggplot2'-style formula string into a small shiny application. Placeholder tokens in the formula become input widgets automatically; the package completes the expression with the current input values, renders the plot, shows the generated code, and supports uploaded datasets in different formats.

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.

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>.

Process in-situ Gamma-Ray Spectrometry for Luminescence Dating. This package allows to import, inspect and correct the energy shifts of gamma-ray spectra. It provides methods for estimating the gamma dose rate by the use of a calibration curve as described in Mercier and Falguères (2007). The package only supports Canberra CNF and TKA and Kromek SPE files.

Comparing two independent or paired groups across a range of descriptive statistics, enabling the evaluation of potential differences in central tendency (mean, median), dispersion (variance, interquartile range), shape (skewness, kurtosis), and distributional characteristics (various quantiles). The analytical framework incorporates parametric t-tests, non-parametric Wilcoxon tests, permutation tests, and bootstrap resampling techniques to assess the statistical significance of observed differences.

This package provides a genomic simulation approach for creating biologically informed individual genotypes from empirical data that 1) samples alleles from populations without replacement, 2) segregates alleles based on species-specific recombination rates. gscramble is a flexible simulation approach that allows users to create pedigrees of varying complexity in order to simulate admixed genotypes. Furthermore, it allows users to track haplotype blocks from the source populations through the pedigrees.

Fits geographically weighted regression (GWR) models and has tools to diagnose and remediate collinearity in the GWR models. Also fits geographically weighted ridge regression (GWRR) and geographically weighted lasso (GWL) models. See Wheeler (2009) <doi:10.1068/a40256> and Wheeler (2007) <doi:10.1068/a38325> for more details.

GitHub apps provide a powerful way to manage fine grained programmatic access to specific git repositories, without having to create dummy users, and which are safer than a personal access token for automated tasks. This package extends the gh package to let you authenticate and interact with GitHub <https://docs.github.com/en/rest/overview> in R as an app.

Triangular and trapezoidal fuzzy numbers are used to study fuzzy logic, fuzzy reasoning and approximating, fuzzy regression models, etc. This package builds the generating function for triangular and trapezoidal fuzzy numbers based on Souliotis et al. (2022)<doi:10.3390/math10183350>. They proposed a method for the construction of fuzzy numbers via a cumulative distribution function based on the possibility theory.

Gene sets are fundamental for gene enrichment analysis. The package geneset enables querying gene sets from public databases including GO (Gene Ontology Consortium. (2004) <doi:10.1093/nar/gkh036>), KEGG (Minoru et al. (2000) <doi:10.1093/nar/28.1.27>), WikiPathway (Marvin et al. (2020) <doi:10.1093/nar/gkaa1024>), MsigDb (Arthur et al. (2015) <doi:10.1016/j.cels.2015.12.004>), Reactome (David et al. (2011) <doi:10.1093/nar/gkq1018>), MeSH (Ish et al. (2014) <doi:10.4103/0019-5413.139827>), DisGeNET (Janet et al. (2017) <doi:10.1093/nar/gkw943>), Disease Ontology (Lynn et al. (2011) <doi:10.1093/nar/gkr972>), Network of Cancer Genes (Dimitra et al. (2019) <doi:10.1186/s13059-018-1612-0>) and COVID-19 (Maxim et al. (2020) <doi:10.21203/rs.3.rs-28582/v1>). Gene sets are stored in the list object which provides data frame of geneset and geneset_name'. The geneset has two columns of term ID and gene ID. The geneset_name has two columns of terms ID and term description.

This package provides functions to read in the geometry format under the Neuroimaging Informatics Technology Initiative ('NIfTI'), called GIFTI <https://www.nitrc.org/projects/gifti/>. These files contain surfaces of brain imaging data.