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.

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.

Allows users to access live UK energy market information via various APIs.

Data from Unicode 17.0.0 and related utilities.

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>.

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

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.

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

This package provides tools for detecting and modeling underdispersion in count data (conditional variance below the conditional mean), the case the Poisson and negative binomial defaults cannot represent. Provides a screening diagnostic that benchmarks at-risk dispersion against a zero-truncated Poisson, regression-adjusted tests of equidispersion, and a dispersion profile that compares the variance-to-mean curves of competing families against the data; the continuous parameter binomial (CPB) and generalized event count (Katz) regressions with zero-truncated, hurdle, and zero-inflated forms and high-dimensional fixed effects with a split-panel jackknife bias correction; matched Poisson, negative binomial, COM-Poisson (rate- and mean-parameterized), generalized Poisson, gamma-count, and double Poisson regressions through the same interface, with frequency weights, offsets, and analytic, robust, and cluster-robust standard errors; bootstrap and profile-likelihood inference; proper scoring rules, rootograms, PIT histograms, and simulation methods; and quantities of interest including predicted distributions, the implied ceiling, rate ratios, and first differences with an extensive/intensive decomposition. The likelihoods are implemented in C++.

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.

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>.

Unit-Gompertz density, cumulative distribution, quantile functions and random deviate generation of the unit-Gompertz distribution. In addition, there are a function for fitting the Generalized Additive Models for Location, Scale and Shape.

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>.

Univariate spline regression. It is possible to add the shape constraint of unimodality and predefined or self-defined penalties on the B-spline coefficients.

This package provides a time series of the national grid demand (high-voltage electric power transmission network) in the UK since 2011.

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).

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.

Download and explore datasets from UCSC Xena data hubs, which are a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others. Databases are normalized so they can be combined, linked, filtered, explored and downloaded.

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.

Provide a set of wrappers to call all the endpoints of UptimeRobot API which includes various kind of ping, keep-alive and speed tests. See <https://uptimerobot.com/> for more information.

This package provides a set of general functions that I have used in various projects and other R packages. Miscellaneous operations on data frames, matrices and vectors, ROC and PR statistics.

Parses HTTP user agent strings and returns user agent, device and OS information. This is a â V8â -backed package that uses the UA, device and OS definitions from the â ua-parserâ project <https://github.com/ua-parser>.

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.

This package implements functions to derive uncertainty intervals for (i) regression (linear and probit) parameters under missing not at random (non-ignorable missingness) as introduced in Genbäck, M., Stanghellini, E., and de Luna, X. (2015) <doi:10.1007/s00362-014-0610-x> and Genbäck, M., Ng, N., Stanghellini, E., and de Luna, X. (2018) <doi:10.1007/s10433-017-0448-x>. Also includes methods for doubly robust and outcome regression estimators of average causal effects under unobserved confounding as in Genbäck, M. and de Luna, X. (2018) <doi:10.1111/biom.13001>, and for partial correlation analysis following Gorbach, T. and de Luna, X. (2018) <doi:10.1016/j.spl.2018.05.027>.