Format text (bold, italic, ...) and numbers using UTF-8. Offers functions to search for emojis and include them in your text.

Most universities use specific color combinations to express their unique brand identity. The unicol package provides the colors and color palettes of various universities for easy plotting and printing in R. We collect and provide a diverse range of color palettes for creating scientific visualizations.

The Universal Scalability Law (Gunther 2007) <doi:10.1007/978-3-540-31010-5> is a model to predict hardware and software scalability. It uses system capacity as a function of load to forecast the scalability for the system.

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).

The Ultimate Microrray Prediction, Reality and Inference Engine (UMPIRE) is a package to facilitate the simulation of realistic microarray data sets with links to associated outcomes. See Zhang and Coombes (2012) <doi:10.1186/1471-2105-13-S13-S1>. Version 2.0 adds the ability to simulate realistic mixed-typed clinical data.

An R API providing easy access to a relational database with macroeconomic, financial and development related time series data for Uganda. Overall more than 5000 series at varying frequency (daily, monthly, quarterly, annual in fiscal or calendar years) can be accessed through the API. The data is provided by the Bank of Uganda, the Ugandan Ministry of Finance, Planning and Economic Development, the IMF and the World Bank. The database is being updated once a month.

Analyzes the impact of external conditions on air quality using counterfactual approaches, featuring methods for data preparation, modeling, and visualization.

This package provides a comprehensive educational package combining clustering algorithms with detailed step-by-step explanations. Provides implementations of both traditional (hierarchical, k-means) and modern (Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Models (GMM), genetic k-means) clustering methods as described in Ezugwu et. al., (2022) <doi:10.1016/j.engappai.2022.104743>. Includes educational datasets highlighting different clustering challenges, based on scikit-learn examples (Pedregosa et al., 2011) <https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html>. Features detailed algorithm explanations, visualizations, and weighted distance calculations for enhanced learning.

Enables the user to calculate Value at Risk (VaR) and Expected Shortfall (ES) by means of various parametric and semiparametric GARCH-type models. For the latter the estimation of the nonparametric scale function is carried out by means of a data-driven smoothing approach. Model quality, in terms of forecasting VaR and ES, can be assessed by means of various backtesting methods such as the traffic light test for VaR and a newly developed traffic light test for ES. The approaches implemented in this package are described in e.g. Feng Y., Beran J., Letmathe S. and Ghosh S. (2020) <https://ideas.repec.org/p/pdn/ciepap/137.html> as well as Letmathe S., Feng Y. and Uhde A. (2021) <https://ideas.repec.org/p/pdn/ciepap/141.html>.

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

Supervised classification methods, which (if asked) can provide step-by-step explanations of the algorithms used, as described in PK Josephine et. al., (2021) <doi:10.59176/kjcs.v1i1.1259>; and datasets to test them on, which highlight the strengths and weaknesses of each technique.

Implementation of the unity forest (UFO) framework (Hornung & Hapfelmeier, 2026, <doi:10.48550/arXiv.2601.07003>). UFOs are a random forest variant designed to better take covariates with purely interaction-based effects into account, including interactions for which none of the involved covariates exhibits a marginal effect. While this framework tends to improve discrimination and predictive accuracy compared to standard random forests, it also facilitates the identification and interpretation of (marginal or interactive) effects: In addition to the UFO algorithm for tree construction, the package includes the unity variable importance measure (unity VIM), which quantifies covariate effects under the conditions in which they are strongest - either marginally or within subgroups defined by interactions - as well as covariate-representative tree roots (CRTRs) that provide interpretable visualizations of these conditions. Categorical and continuous outcomes are supported. This package is a fork of the R package ranger (main author: Marvin N. Wright), which implements random forests using an efficient C++ backend.

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>.

United is a software tool which can be downloaded at the following website <http://www.schroepl.net/pbm/software/united/>. In general, it is a virtual manager game for football teams. This package contains helpful functions for determining an optimal formation for a virtual match in United. E.g. knowing that the opponent has a strong defensive it is advisable to beat him in the midfield. Furthermore, this package contains functions for computing the optimal usage of hardness in a game.

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).

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>.

Dataset contains select attributes for each match result since 1949-1950 season for UNC men's basketball team.

In many phase I trials, the design goal is to find the dose associated with a certain target toxicity rate. In some trials, the goal can be to find the dose with a certain weighted sum of rates of various toxicity grades. For others, the goal is to find the dose with a certain mean value of a continuous response. This package provides the setup and calculations needed to run a dose-finding trial with non-binary endpoints and performs simulations to assess designâ s operating characteristics under various scenarios. Three dose finding designs are included in this package: unified phase I design (Ivanova et al. (2009) <doi:10.1111/j.1541-0420.2008.01045.x>), Quasi-CRM/Robust-Quasi-CRM (Yuan et al. (2007) <doi:10.1111/j.1541-0420.2006.00666.x>, Pan et al. (2014) <doi:10.1371/journal.pone.0098147>) and generalized BOIN design (Mu et al. (2018) <doi:10.1111/rssc.12263>). The toxicity endpoints can be handled with these functions including equivalent toxicity score (ETS), total toxicity burden (TTB), general continuous toxicity endpoints, with incorporating ordinal grade toxicity information into dose-finding procedure. These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose-limiting toxicity (DLT) rates, discrete or continuous toxicity score, and incorporate safety and/or stopping rules.

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

This package provides S3 generic methods and some default implementations for Bayesian analyses that generate Markov Chain Monte Carlo (MCMC) samples. The purpose of universals is to reduce package dependencies and conflicts. The nlist package implements many of the methods for its nlist class.

In diagnostic contexts, individuals are often assessed using multiple tests that measure the same latent variable (e.g., intelligence). These test scores are typically not exactly identical. Simple averaging neglects the correlation between tests and the reduced variance of their combination. The unifyR package provides functions to compute statistically accurate unified scores, reliabilities and validities of multiple tests. The underlying algorithms build on and extend the method proposed by Evans (1996, <DOI:10.3758/BF03204767>) and have been validated through simulations.

Construct and plot objective hierarchies and associated value and utility functions. Evaluate the values and utilities and visualize the results as colored objective hierarchies or tables. Visualize uncertainty by plotting median and quantile intervals within the nodes of objective hierarchies. Get numerical results of the evaluations in standard R data types for further processing.

This package provides a suite of utilities for working with the UK Biobank <https://www.ukbiobank.ac.uk/> Nuclear Magnetic Resonance spectroscopy (NMR) metabolomics data <https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=220>. Includes functions for extracting biomarkers from decoded UK Biobank field data, removing unwanted technical variation from biomarker concentrations, computing an extended set of lipid, fatty acid, and cholesterol fractions, and for re-deriving composite biomarkers and ratios after adjusting data for unwanted biological variation. For further details on methods see Ritchie SC et al. Sci Data (2023) <doi:10.1038/s41597-023-01949-y>.

Generate, parse, and validate RFC 9562 UUIDs from R using the Rust uuid crate via extendr'. Developed by Thomas Bryce Kelly at Icy Seas Co-Laboratory LLC. Version 7 UUIDs are the default for new identifiers, while versions 4, 5, 6, and legacy version 1 are also supported. Functions return character vectors by default and can also expose 16-byte raw representations for low-level workflows.