This package provides a ggplot2 theme and color palettes following the United Nations High Commissioner for Refugees (UNHCR) Data Visualization Guidelines recommendations.

Full listing of UK baby names occurring more than three times per year between 1974 and 2020, and rankings of baby name popularity by decade from 1904 to 1994.

Seven documented data sets from transport, traffic safety, urban planning, construction and architectural engineering. The package provides fixed, redistributable snapshots with consistent variable names. Each help page records the source, licence, unit of observation, transformations and limitations of its data set. Sources include Yeh (2018) <doi:10.24432/C5J30W>, Tsanas and Xifara (2012) <doi:10.24432/C51307>, Yeh (1998) <doi:10.24432/C5PK67>, Seoul Bike Sharing Demand (2020) <doi:10.24432/C5F62R>, and Singh and Chaudhari (2018) <doi:10.24432/C5P605>.

This package provides an algorithm to detect and characterize disturbances (start, end dates, intensity) that can occur at different hierarchical levels by studying the dynamics of longitudinal observations at the unit level and group level based on Nadaraya-Watson's smoothing curves, but also a shiny app which allows to visualize the observations and the detected disturbances. Finally the package provides a dataframe mimicking a pig farming system subsected to disturbances simulated according to Le et al.(2022) <doi:10.1016/j.animal.2022.100496>.

Interface to easily access data via the United States Department of Agriculture (USDA)'s Livestock Mandatory Reporting ('LMR') Data API at <https://mpr.datamart.ams.usda.gov/>. The downloaded data can be saved for later off-line use. Also provide relevant information and metadata for each of the input variables needed for sending the data inquiry.

This package provides easy access to a curated selection of pre-processed data sets relevant to the COVID-19 outbreak in the UK for teaching and demonstration purposes.

This package provides the ability to read Unisens data into R. Unisens is a universal data format for multi sensor data.

This package provides an R interface to the Python package u-stats <https://pypi.org/project/u-stats/> for efficient computation of higher-order U-statistics using Einstein summation notation, implementing the methods of Chen, Zhang, and Liu (2025) <doi:10.48550/arXiv.2508.12627>. The package automatically converts R objects to NumPy or PyTorch tensors via reticulate and supports GPU acceleration when PyTorch with CUDA is available. Python dependencies are declared via reticulate and can be installed automatically on first use. Designed for large-scale statistical estimation where numerical stability and performance are critical.

This package provides a classification (decision) tree is constructed from survival data with high-dimensional covariates. The method is a robust version of the logrank tree, where the variance is stabilized. The main function "uni.tree" returns a classification tree for a given survival dataset. The inner nodes (splitting criterion) are selected by minimizing the P-value of the two-sample the score tests. The decision of declaring terminal nodes (stopping criterion) is the P-value threshold given by an argument (specified by user). This tree construction algorithm is proposed by Emura et al. (2021, in review).

Scores standardized patient-reported instruments used in urology and pelvic health research, including the International Prostate Symptom Score (Barry et al., 1992), the Overactive Bladder Symptom Score (Homma et al., 2006) <doi:10.1016/j.urology.2006.02.042>, the O'Leary-Sant interstitial cystitis indices, the short forms of the Urogenital Distress Inventory and the Incontinence Impact Questionnaire (Uebersax et al., 1995) <doi:10.1002/nau.1930140206>, the Sandvik incontinence severity index, and the Benign Prostatic Hyperplasia Impact Index. Instruments are declarative definitions read by a single scoring engine. Responses are checked against the permitted value set of each item, missing items follow the published rule for the instrument or return NA when none was published, severity bands are assigned by membership, and published minimal important difference statistics are included for responder analyses.

This package provides implementations of some of the most important outlier detection algorithms. Includes a tutorial mode option that shows a description of each algorithm and provides a step-by-step execution explanation of how it identifies outliers from the given data with the specified input parameters. References include the works of Azzedine Boukerche, Lining Zheng, and Omar Alfandi (2020) <doi:10.1145/3381028>, Abir Smiti (2020) <doi:10.1016/j.cosrev.2020.100306>, and Xiaogang Su, Chih-Ling Tsai (2011) <doi:10.1002/widm.19>.

Calculates the Urban Centrality Index (UCI) as in Pereira et al., (2013) <doi:10.1111/gean.12002>. The UCI measures the extent to which the spatial organization of a city or region varies from extreme polycentric to extreme monocentric in a continuous scale from 0 to 1. Values closer to 0 indicate more polycentric patterns and values closer to 1 indicate a more monocentric urban form.

This package provides tools for clustering individualized survival curves using the Partitioning Around Medoids (PAM) algorithm, with monotonic enforcement, optional smoothing, weighted distances (L1/L2), automatic K selection via silhouette width, prediction for new curves, basic stability checks, and plotting helpers. The method is described in El Badisy (2026) <doi:10.1093/bioadv/vbag218>.

Up-and-Down (UD) is the most popular design approach for dose-finding, but it has been severely under-served by the statistical and computing communities. This is the first package that comprehensively addresses UD's needs. Recent applied UD tutorial: Oron et al., 2022 <doi:10.1097/ALN.0000000000004282>. Recent methodological overview: Oron and Flournoy, 2024 <doi:10.51387/24-NEJSDS74>.

This package provides a simple interface to the Geographic Header information from the "2010 US Census Summary File 2". The entire Summary File 2 is described at <https://catalog.data.gov/dataset/census-2000-summary-file-2-sf2>, but note that this package only provides access to parts of the geographic header ('geoheader') of the file. In particular, only the first 101 columns of the geoheader are included and, more importantly, only rows with summary levels (SUMLEVs) 010 through 050 (nation down through county level) are included. In addition to access to (part of) the geoheader, the package also provides a decode function that takes a column name and value and, for certain columns, returns "the meaning" of that column (i.e., a "SUMLEV" value of 40 means "State"); without a value, the decode function attempts to describe the column itself.

Consistent with knitr syntax highlighting, usedthese adds a summary table of package & function usage to a Quarto document and enables aggregation of usage across a website.

This package provides a diverse collection of U.S. datasets encompassing various fields such as crime, economics, education, finance, energy, healthcare, and more. It serves as a valuable resource for researchers and analysts seeking to perform in-depth analyses and derive insights from U.S.-specific data.

This package provides tools package to extract and analyze data from U SPORTS, the governing body of university sport in Canada.

Implement a shrinkage estimation for the univariate normal mean based on a preliminary test (pretest) estimator. This package also provides the confidence interval based on pivoting the cumulative density function. The methodologies are published in Taketomi et al.(2024) <doi:10.1007/s42081-023-00221-2> and Taketomi et al.(2024-)(under review).

Verb-like functions to work with messy data, often derived from spreadsheets or parsed PDF tables. Includes functions for unwrapping values broken up across rows, relocating embedded grouping values, and to annotate meaningful formatting in spreadsheet files.

Automatically converts language-specific verbal information, e.g., "1st half of the 19th century," to its standardized numerical counterparts, e.g., "1801-01-01/1850-12-31." It follows the recommendations of the MIDAS ('Marburger Informations-, Dokumentations- und Administrations-System'), see <doi:10.11588/artdok.00003770>.

Programmatic interface to access data from the UK Health Security Agency (UKHSA) Data Dashboard API. The package was originally based on the ukcovid19 package by Pouria Hadjibagheri and has been substantially rewritten and extended. For more information on the API, see <https://ukhsa-dashboard.data.gov.uk/access-our-data>.

This package provides functions for uniform sampling of the environmental space, designed to assist species distribution modellers in gathering ecologically relevant pseudo-absence data. The method ensures balanced representation of environmental conditions and helps reduce sampling bias in model calibration. Based on the framework described by Da Re et al. (2023) <doi:10.1111/2041-210X.14209>.

Demographic data on the United States at the county and state levels spanning multiple years.