Build regular expressions piece by piece using human readable code. This package contains Unicode functionality, and is primarily intended to be used by package developers.

Reads, writes and validates mzQC files. The mzQC format is a standardized file format for the exchange, transmission, and archiving of quality metrics derived from biological mass spectrometry data, as defined by the HUPO-PSI (Human Proteome Organisation - Proteomics Standards Initiative) Quality Control working group. See <https://hupo-psi.github.io/mzQC/> for details.

This package provides the user with functions to develop their trading strategy, uncover actionable trading ideas, and monitor consensus shifts with crowdsourced earnings and economic estimate data directly from <www.estimize.com>. Further information regarding the web services this package invokes can be found at <www.estimize.com/api>.

This package provides functions to write messages to the syslog system logger API, available on all POSIX'-compatible operating systems. Features include tagging messages with a priority level and application type, as well as masking (hiding) messages below a given priority level.

We provide a variety of algorithms for manifold-valued data, including Fréchet summaries, hypothesis testing, clustering, visualization, and other learning tasks. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> for general exposition to statistics on manifolds.

This package provides a convenient way to read fixed-width ASCII polling datasets from providers like the Roper Center <https://ropercenter.cornell.edu>.

Extends the functionality of the RTMB <https://kaskr.r-universe.dev/RTMB> package by providing a collection of non-standard probability distributions compatible with automatic differentiation (AD). While RTMB enables flexible and efficient modelling, including random effects, its built-in support is limited to standard distributions. The package adds additional AD-compatible distributions, broadening the range of models that can be implemented and estimated using RTMB'. Automatic differentiation and Laplace approximation are described in Kristensen et al. (2016) <doi:10.18637/jss.v070.i05>.

An implementation of the Heroicons icon library for shiny applications and other R web-based projects. You can search, render, and customize icons without CSS or JavaScript dependencies.

Reads river network shape files and computes network distances. Also included are a variety of computation and graphical tools designed for fisheries telemetry research, such as minimum home range, kernel density estimation, and clustering analysis using empirical k-functions with a bootstrap envelope. Tools are also provided for editing the river networks, meaning there is no reliance on external software.

Interactive data tables for R, based on the React Table JavaScript library. Provides an HTML widget that can be used in R Markdown or Quarto documents, Shiny applications, or viewed from an R console.

Implementation of the relative placement algorithm widely used in the scoring of Lindy Hop and West Coast Swing dance contests.

This package performs robust estimation and inference when using covariate adjustment and/or covariate-adaptive randomization in randomized clinical trials. Ting Ye, Jun Shao, Yanyao Yi, Qinyuan Zhao (2023) <doi:10.1080/01621459.2022.2049278>. Ting Ye, Marlena Bannick, Yanyao Yi, Jun Shao (2023) <doi:10.1080/24754269.2023.2205802>. Ting Ye, Jun Shao, Yanyao Yi (2023) <doi:10.1093/biomet/asad045>. Marlena Bannick, Jun Shao, Jingyi Liu, Yu Du, Yanyao Yi, Ting Ye (2024) <doi:10.1093/biomet/asaf029>. Xiaoyu Qiu, Yuhan Qian, Jaehwan Yi, Jinqiu Wang, Yu Du, Yanyao Yi, Ting Ye (2025) <doi:10.48550/arXiv.2408.12541>.

This package provides simplified methods for managing classic Rubik's cubes and many other modifications of it (such as NxNxN size cubes, void cubes and 8-coloured cubes - so called octa cubes). Includes functions of handling special syntax for managing such cubes; and different approach to plotting 3D cubes without using external libraries (for example OpenGL').

This package provides robust methods to detect change-points in uni- or multivariate time series. They can cope with corrupted data and heavy tails. Focus is on the detection of abrupt changes in location, but changes in the scale or dependence structure can be detected as well. This package provides tests for change detection in uni- and multivariate time series based on Huberized versions of CUSUM tests proposed in Duerre and Fried (2019) <DOI:10.48550/arXiv.1905.06201>, and tests for change detection in univariate time series based on 2-sample U-statistics or 2-sample U-quantiles as proposed by Dehling et al. (2015) <DOI:10.1007/978-1-4939-3076-0_12> and Dehling, Fried and Wendler (2020) <DOI:10.1093/biomet/asaa004>. Furthermore, the packages provides tests on changes in the scale or the correlation as proposed in Gerstenberger, Vogel and Wendler (2020) <DOI:10.1080/01621459.2019.1629938>, Dehling et al. (2017) <DOI:10.1017/S026646661600044X>, and Wied et al. (2014) <DOI:10.1016/j.csda.2013.03.005>.

The goal of rFIA is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, rFIA simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, rFIA improves accessibility to the spatial-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., dplyr', tidyr', and sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) <doi:10.2737/SRS-GTR-80>, and has been validated against estimates and sampling errors produced by FIA EVALIDator'. Current development is focused on the implementation of spatially-enabled model-assisted and model-based estimators to improve population, change, and ratio estimates.

Providing the container for the DockerParallel package.

Climacell is a weather platform that provides hyper-local forecasts and weather data. This package enables the user to query the core layers of the time line interface of the Climacell v4 API <https://www.climacell.co/weather-api/>. This package requires a valid API key. See vignettes for instructions on use.

This package provides streamlined functions for summarising and visualising regression models fitted with the rms package, in the preferred format for medical journals. The modelsummary_rms() function produces concise summaries for linear, logistic, and Cox regression models, including automatic handling of models containing restricted cubic spline (RCS) terms. The resulting summary dataframe can be easily converted into publication-ready documents using the flextable and officer packages. The ggrmsMD() function creates clear and customizable plots ('ggplot2 objects) to visualise RCS terms.

We implement linear regression when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked, based on D'Haultfoeuille, Gaillac, Maurel (2022) <doi:10.3386/w29953>. The package allows for common regressors observed in both datasets, and for various shape constraints on the effect of covariates on the outcome of interest. It also provides the tools to perform a test of point identification. See the associated vignette <https://github.com/cgaillac/RegCombin/blob/master/RegCombin_vignette.pdf> for theory and code examples.

Bootstrap forecast densities for GARCH (Generalized Autoregressive Conditional Heteroskedastic) returns and volatilities using the robust residual-based bootstrap procedure of Trucios, Hotta and Ruiz (2017) <DOI:10.1080/00949655.2017.1359601>.

Parameter estimation, computation of probability, information, and (log-)likelihood, and visualization of item/test characteristic curves and item/test information functions for three uni-dimensional item response theory models: the 3-parameter-logistic model, generalized partial credit model, and graded response model. The full documentation and tutorials are at <https://github.com/xluo11/Rirt>.

Efficient framework for ridge redundancy analysis (rrda), tailored for high-dimensional omics datasets where the number of predictors exceeds the number of samples. The method leverages Singular Value Decomposition (SVD) to avoid direct inversion of the covariance matrix, enhancing scalability and performance. It also introduces a memory-efficient storage strategy for coefficient matrices, enabling practical use in large-scale applications. The package supports cross-validation for selecting regularization parameters and reduced-rank dimensions, making it a robust and flexible tool for multivariate analysis in omics research. Please refer to our article (Yoshioka et al., 2025) for more details.

Researchers commonly need to summarize scientific information, a process known as evidence synthesis'. The first stage of a synthesis process (such as a systematic review or meta-analysis) is to download a list of references from academic search engines such as Web of Knowledge or Scopus'. The traditional approach to systematic review is then to sort these data manually, first by locating and removing duplicated entries, and then screening to remove irrelevant content by viewing titles and abstracts (in that order). revtools provides interfaces for each of these tasks. An alternative approach, however, is to draw on tools from machine learning to visualise patterns in the corpus. In this case, you can use revtools to render ordinations of text drawn from article titles, keywords and abstracts, and interactively select or exclude individual references, words or topics.

Finds the k nearest neighbours for every point in a given dataset using Jose Luis nanoflann library. There is support for exact searches, fixed radius searches with kd trees and two distances, the Euclidean and Manhattan'. For more information see <https://github.com/jlblancoc/nanoflann>. Also, the nanoflann library is exported and ready to be used via the linking to mechanism.