This package provides functions for the creation and manipulation of scenes and objects within the Unity 3D video game engine (<https://unity.com/>). Specific focuses include the creation and import of terrain data and GameObjects as well as scene management.

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.

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

Fit Bayesian hierarchical models of animal abundance and occurrence via the rstan package, the R interface to the Stan C++ library. Supported models include single-season occupancy, dynamic occupancy, and N-mixture abundance models. Covariates on model parameters are specified using a formula-based interface similar to package unmarked', while also allowing for estimation of random slope and intercept terms. References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.

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.

Detects values imported from spreadsheets that were auto-converted to Excel date serials and reconstructs the originally intended day.month decimals (for example, 30.3 that Excel displayed as 30/03/2025'). The functions work in a vectorized manner, preserve non-serial values, and support both the 1900 and 1904 date systems.

Updated versions of the 1970s "US State Facts and Figures" objects from the datasets package included with R. The new data is compiled from a number of sources, primarily from the United States Census Bureau or the relevant federal agency. Modern tidy tibbles provide richer state-level data including identifiers, geography, capitals, demographics, and socioeconomic statistics. Convenience vectors parallel the base datasets state objects but extend coverage to all 51 jurisdictions: the 50 states and the District of Columbia.

This package provides a variational mapping approach that reveals and expands future temporal dynamics from folded high-dimensional geometric distance spaces, unfold turns a set of time series into a 4D block of pairwise distances between reframed windows, learns a variational mapper that maps those distances to the next reframed window, and produces horizon-wise predictive functions for each input series. In short: it unfolds the future path of each series from a folded geometric distance representation.

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.

Reconstructs all possible raw data that could have led to reported summary statistics. Provides a wrapper for the Rust implementation of the CLOSURE algorithm.

This package provides tools for analyzing sequencing data containing unique molecular identifiers generated by UMIErrorCorrect (<https://github.com/stahlberggroup/umierrorcorrect>).

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

The "ussher" data set is drawn from original chronological textual historic events. Commonly known as James Ussher's Annals of the World, the source text was originally written in Latin in 1650, and published in English translation in 1658.The data are classified by index, year, epoch (or one of the 7 ancient "Ages of the World"), Biblical source book if referenced (rarely), as well as alternate dating mechanisms, such as "Anno Mundi" (age of the world) or "Julian Period" (dates based upon the Julian calendar). Additional file "usshfull" includes variables that may be of further interest to historians, such as Southern Kingdom and Northern Kingdom discrepant dates, and the original amalgamated dating mechanic used by Ussher in the original text. The raw data can also be called using "usshraw", as described in: Ussher, J. (1658) <https://archive.org/stream/AnnalsOfTheWorld/Annals_djvu.txt>.

Fit a univariate-guided sparse regression (lasso), by a two-stage procedure. The first stage fits p separate univariate models to the response. The second stage gives more weight to the more important univariate features, and preserves their signs. Conveniently, it returns an objects that inherits from class glmnet', so that all of the methods for glmnet are available. See Chatterjee, Hastie and Tibshirani (2025) <doi:10.1162/99608f92.c79ff6db> for details.

Assess essential unidimensionality using external validity information using the procedure proposed by Ferrando & Lorenzo-Seva (2019) <doi:10.1177/0013164418824755>. Provides two indices for assessing differential and incremental validity, both based on a second-order modelling schema for the general factor.

This package provides an R-based access to the datasets including their resources from the portal <https://umwelt.info>. The package allows for an easy integration of those datasets into your R-based workflows. The functionality of the package mirrors the web-based access as provided at <https://umwelt.info>. You can use the same queries and get the same datasets by accessing our API.

Does uniformly most powerful (UMP) and uniformly most powerful unbiased (UMPU) tests. At present only distribution implemented is binomial distribution. Also does fuzzy tests and confidence intervals (following Geyer and Meeden, 2005, <doi:10.1214/088342305000000340>) for the binomial distribution (one-tailed procedures based on UMP test and two-tailed procedures based on UMPU test).

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>.

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>.

This package provides functions for building customized ready-to-export tables for publication.

This program realizes a universal estimation approach that accommodates multi-category variables and effect scales, making up for the deficiencies of the existing approaches when dealing with non-binary exposures and complex models. The estimation via bootstrapping can simultaneously provide results of causal mediation on risk difference (RD), odds ratio (OR) and risk ratio (RR) scales with tests of the effects difference. The estimation is also applicable to many other settings, e.g., moderated mediation, inconsistent covariates, panel data, etc. The high flexibility and compatibility make it possible to apply for any type of model, greatly meeting the needs of current empirical researches.

This package provides a unified R6-based interface for various machine learning models with automatic interface detection, consistent cross-validation, model interpretations via numerical derivatives, and visualization. Supports both regression and classification tasks with any model function that follows R's standard modeling conventions (formula or matrix interface).

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>.