This package provides methods to help selecting General Circulation Models (GCMs) in the context of projecting models to future scenarios. It is provided clusterization algorithms, distance and correlation metrics, as well as a tailor-made algorithm to detect the optimum subset of GCMs that recreate the environment of all GCMs as proposed in Esser et al. (2025) <doi:10.1111/gcb.70008>.

When visualising changes between two values over time, a strict linear interpolation can look jarring and unnatural. By applying a non-linear easing to the transition, the motion between values can appear smoother and more natural. This package includes functions for applying such non-linear easings to colors and numeric values, and is useful where smooth animated movement and transitions are desired.

This package provides a direct approach to optimal designs for copula models based on the Fisher information. Provides flexible functions for building joint PDFs, evaluating the Fisher information and finding optimal designs. It includes an extensible solution to summation and integration called nint', functions for transforming, plotting and comparing designs, as well as a set of tools for common low-level tasks.

This package provides a method to calculate the distance to the climatic tree line for large data sets of coordinates (World Geodetic System 1984) with geographical uncertainty. The default thresholds and the treeline definition is based on Paulsen and Körner (2014) <doi:10.1007/s00035-014-0124-0>, users are free to decide what climate layers they would like to use.

This package provides a small group of functions to read in a data dictionary and the corresponding data table from Excel and to automate the cleaning, re-coding and creation of simple calculated variables. This package was designed to be a companion to the macro-enabled Excel template available on the GitHub site, but works with any similarly-formatted Excel data.

This package provides easy-to-understand and consistent interfaces for accessing data on the U.S. Congress. The functions in filibustr streamline the process for importing data on Congress into R, removing the need to download and work from CSV files and the like. Data sources include Voteview (<https://voteview.com/>), the U.S. Senate website (<https://www.senate.gov/>), and more.

Moon charts are like pie charts except that the proportions are shown as crescent or gibbous portions of a circle, like the lit and unlit portions of the moon. As such, they work best with only one or two groups. gggibbous extends ggplot2 to allow for plotting multiple moon charts in a single panel and does not require a square coordinate system.

Work with model files (setup, input, output) from the hydrological catchment model HYPE: Streamlined file import and export, standard evaluation plot routines, diverse post-processing and aggregation routines for hydrological model analysis. The HYPEtools package is also archived at <doi:10.5281/zenodo.7627955> and can be cited in publications with Brendel et al. (2024) <doi:10.1016/j.envsoft.2024.106094>.

Calculate multiple statistics with confidence intervals for matched case-control data including risk difference, risk ratio, relative difference, and the odds ratio. Results are equivalent to those from Stata', and you can choose how to format your input data. Methods used are those described on page 56 the Stata documentation for "Epitab - Tables for Epidemologists" <https://www.stata.com/manuals/repitab.pdf>.

Fit mixture of Markov chains of higher orders from multiple sequences. It is also compatible with ordinary 1-component, 1-order or single-sequence Markov chains. Various utility functions are provided to derive transition patterns, transition probabilities per component and component priors. In addition, print(), predict() and component extracting/replacing methods are also defined as a convention of mixture models.

This package implements a novel density-based approach for estimating unknown means, visualizing distributions, and meta-analyses of quantiles. A detailed vignettes with example datasets and code to prepare data and analyses is available at <https://bookdown.org/a2delivera/metaquant/>. The methods are described in the pre-print by De Livera, Prendergast and Kumaranathunga (2024, <doi:10.48550/arXiv.2411.10971>).

It is often useful when developing an R package to track the relationship between functions in order to appropriately test and track changes. This package generates a graph of the relationship between all R functions in a package. It can also be used on any directory containing .R files which can be very useful for shiny apps or other non-package workflows.

This algorithm classifies the trends into linear, quadratic, cubic, concealed and no-trend types. The "concealed trends" are those trends that possess quadratic or cubic forms, but the net change from the start of the time period to the end of the time period hasn't been significant. The "no-trend" category includes simple linear trends with statistically in-significant slope coefficient.

Visualizes panel data. It has three main functionalities: (1) it plots the treatment status and missing values in a panel dataset; (2) it visualizes the temporal dynamics of a main variable of interest; (3) it depicts the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate. For details, see <doi:10.18637/jss.v107.i07>.

This package provides functions to load Research Patient Data Registry ('RPDR') text queries from Partners Healthcare institutions into R. The package also provides helper functions to manipulate data and execute common procedures such as finding the closest radiological exams considering a given timepoint, or creating a DICOM header database from the downloaded images. All functionalities are parallelized for fast and efficient analyses.

The superdiag package provides a comprehensive test suite for testing Markov Chain nonconvergence. It integrates five standard empirical MCMC convergence diagnostics (Gelman-Rubin, Geweke, Heidelberger-Welch, Raftery-Lewis, and Hellinger distance) and plotting functions for trace plots and density histograms. The functions of the package can be used to present all diagnostic statistics and graphs at once for conveniently checking MCMC nonconvergence.

These are miscellaneous functions that I find useful for my research and teaching. The contents include themes for plots, functions for simulating quantities of interest from regression models, functions for simulating various forms of fake data for instructional/research purposes, and many more. All told, the functions provided here are broadly useful for data organization, data presentation, data recoding, and data simulation.

This package provides a collection of high-performance functions for the triangular distribution that consists of the probability density function, cumulative distribution function, quantile function, random variate generator, moment generating function, characteristic function, and expected shortfall function. References: Samuel Kotz, Johan Ren Van Dorp (2004) <doi:10.1142/5720> and Acerbi, Carlo and Tasche, Dirk. (2002) <doi:10.1111/1468-0300.00091>.

Determine the path of the executing script. Compatible with several popular GUIs: Rgui', RStudio', Positron', VSCode', Jupyter', Emacs', and Rscript (shell). Compatible with several functions and packages: source()', sys.source()', debugSource() in RStudio', compiler::loadcmp()', utils::Sweave()', box::use()', knitr::knit()', plumber::plumb()', shiny::runApp()', package:targets', and testthat::source_file()'.

This package contains several utility functions for manipulating tensor-valued data (centering, multiplication from a single mode etc.) and the implementations of the following blind source separation methods for tensor-valued data: tPCA', tFOBI', tJADE', k-tJADE', tgFOBI', tgJADE', tSOBI', tNSS.SD', tNSS.JD', tNSS.TD.JD', tPP and tTUCKER'.

Efficient tabulation with Stata-like output. For each unique value of the variable, it shows the number of observations with that value, proportion of observations with that value, and cumulative proportion, in descending order of frequency. Accepts data.table, tibble, or data.frame as input. Efficient with big data: if you give it a data.table, tab() uses data.table syntax.

Non- and semiparametric regression for generalized additive, partial linear, and varying coefficient models as well as their combinations via smoothed backfitting. Based on Roca-Pardinas J and Sperlich S (2010) <doi:10.1007/s11222-009-9130-2>; Mammen E, Linton O and Nielsen J (1999) <doi:10.1214/aos/1017939138>; Lee YK, Mammen E, Park BU (2012) <doi:10.1214/12-AOS1026>.

This package contains infrastructure for benchmarking analysis methods and access to single cell mixture benchmarking data. It provides a framework for organising analysis methods and testing combinations of methods in a pipeline without explicitly laying out each combination. It also provides utilities for sampling and filtering SingleCellExperiment objects, constructing lists of functions with varying parameters, and multithreaded evaluation of analysis methods.

`orthogene` is an R package for easy mapping of orthologous genes across hundreds of species. It pulls up-to-date gene ortholog mappings across **700+ organisms**. It also provides various utility functions to aggregate/expand common objects (e.g. data.frames, gene expression matrices, lists) using **1:1**, **many:1**, **1:many** or **many:many** gene mappings, both within- and between-species.