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

Probability functions, family for glm() and Stan code for working with the unifed distribution (Quijano Xacur, 2019; <doi:10.1186/s40488-019-0102-6>).

This package provides a test to understand the stability of the underlying stochastic data. Helps the userâ s understand whether the random variable under consideration is stationary or non-stationary without any manual interpretation of the results. It further ensures to check all the prerequisites and assumptions which are underlying the unit root test statistics and if the underlying data is found to be non-stationary in all the 4 lags the function diagnoses the input data and returns with an optimised solution on the same.

Predicts a smooth and continuous (individual) utility function from utility points, and computes measures of intensity for risk and higher-order risk measures (or any other measure computed with user-written function) based on this utility function and its derivatives according to the method introduced in Schneider (2017) <http://hdl.handle.net/21.11130/00-1735-0000-002E-E306-0>.

In the field of systems biology, chemical reaction networks are modeled in various ways, two of those are: (i) stochastic simulations (e.g. Gillespie algorithm) and (ii) ordinary differential equations. In this package we use a simple tabular model description of reaction systems and automatically generate C code for either solver type. We use the ordinary differential equation solvers from the GNU Scientific Library and provide an interface that deals with lists of simulation experiments. Each simulation experiment contains both the data, and instructions for the model to replicate the data. We use approximate Bayesian computation methods (combined with Markov chain Monte Carlo and sequential Monte Carlo, particle filters) as well as classic methods such as Random Walk Metropolis (Gaussian transition kernel) and Simplified Manifold Metropolis adjusted Langevin algorithm for a Bayesian investigation of the model´s parameter space. Experiments can be evaluated in a sequence; intermediate probability densities are modeled using the VineCopula package. The package is also intended to be useful in an HPC environment, with some functions that use pbdMPI capabilities.

Data from Unicode 17.0.0 and related utilities.

This package provides a tool for checking how much information is disclosed when reporting summary statistics.

This package provides a set of functions leading to multivariate response L1 regression. This includes functions on computing Euclidean inner products and norms, weighted least squares estimates on multivariate responses, function to compute fitted values and residuals. This package is a companion to the book "U-Statistics, M-estimation and Resampling", by Arup Bose and Snigdhansu Chatterjee, to appear in 2017 as part of the "Texts and Readings in Mathematics" (TRIM) series of Hindustan Book Agency and Springer-Verlag.

Calculates a Mahalanobis distance for every row of a set of outcome variables (Mahalanobis, 1936 <doi:10.1007/s13171-019-00164-5>). The conditional Mahalanobis distance is calculated using a conditional covariance matrix (i.e., a covariance matrix of the outcome variables after controlling for a set of predictors). Plotting the output of the cond_maha() function can help identify which elements of a profile are unusual after controlling for the predictors.

Separate a data frame in two based on key columns. The function unjoin() provides an inside-out version of a nested data frame. This is used to identify duplication and normalize it (in the database sense) by linking two tables with the redundancy removed. This is a basic requirement for detecting topology within spatial structures that has motivated the need for this package as a building block for workflows within more applied projects.

An R client to fetch SDMX (Statistical Data and Metadata eXchange) CSV series from the UNICEF Data Warehouse <https://data.unicef.org/>. Part of a trilingual suite also available for Python and Stata'. Features include automatic pagination, caching with memoisation, country name lookups, metadata versioning (vintages), and comprehensive indicator support for SDG (Sustainable Development Goals) monitoring.

This package provides a modern C++17/ reimplementation of the UCMINF/ algorithm for unconstrained nonlinear optimization (Nielsen and Mortensen, 2011, <doi:10.32614/CRAN.package.ucminf>), offering full API compatibility with the original ucminf R package but developed independently. The optimizer core has been rewritten in C with a modern header-only C++17 interface, zero-allocation line search, and an Rcpp interface. The goal is numerical equivalence with improved performance, reproducibility, and extensibility. Includes extensive test coverage, performance regression tests, and compatibility checks against ucminf'. This package is not affiliated with the original maintainers but acknowledges their authorship of the algorithm and the original R interface.

This package provides geospatial data for Uruguay, including country boundaries and administrative divisions (departments). Data are provided as sf objects ready for use with the sf and ggplot2 packages.

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.

Basic statistical analyses. The package provides comprehensive functions and datasets for teaching introductory statistics courses. It has been developed to be used in undergraduate statistics courses at Bocconi University (Milan, Italy), and constitutes the core software tool used throughout the textbook by Piccarreta, R., Tonini, D., & Trentini, F. (2026) "From Data to Decisions. An Introduction to Applied Statistics", BUP, ISBN:9788823824096.

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>.

Define and use graphical elements of corporate design manuals in R. The unikn package provides color functions (by defining dedicated colors and color palettes, and commands for finding, changing, viewing, and using them) and styled text elements (e.g., for marking, underlining, or plotting colored titles). The pre-defined range of colors and text decoration functions is based on the corporate design of the University of Konstanz <https://www.uni-konstanz.de/>, but can be adapted and extended for other purposes or institutions.

Code snippets to fit models using the tidymodels framework can be easily created for a given data set.

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

User-friendly maximum likelihood estimation (Fisher (1921) <doi:10.1098/rsta.1922.0009>) of univariate densities.

This package provides functions and a Shiny application for downloading, analyzing and visualizing datasets from UCSC Xena (<http://xena.ucsc.edu/>), which is a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others.

S3 classes and methods for manipulation with georeferenced raster data: reading/writing, processing, multi-panel visualization.

Nonparametric estimation of a unimodal or U-shape covariate effect under additive hazards model.

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>.