This package provides functions to implement the methods of the Flood Estimation Handbook (FEH), associated updates and the revitalised flood hydrograph model (ReFH). Currently the package uses NRFA peak flow dataset version 15. Aside from FEH functionality, further hydrological functions are available. Most of the methods implemented in this package are described in one or more of the following: "Flood Estimation Handbook", Centre for Ecology & Hydrology (1999, ISBN:0 948540 94 X). "Flood Estimation Handbook Supplementary Report No. 1", Kjeldsen (2007, ISBN:0 903741 15 7). "Regional Frequency Analysis - an approach based on L-moments", Hosking & Wallis (1997, ISBN: 978 0 521 01940 8). "Making better use of local data in flood frequency estimation", Environment Agency (2017, ISBN: 978 1 84911 387 8). "Sampling uncertainty of UK design flood estimation" , Hammond (2021, <doi:10.2166/nh.2021.059>). "The FEH 2025 statistical method update", UK Centre for Ecology and Hydrology (2025). "Low flow estimation in the United Kingdom", Institute of Hydrology (1992, ISBN 0 948540 45 1). Data from the UK National River Flow Archive (<https://nrfa.ceh.ac.uk/>, terms and conditions: <https://nrfa.ceh.ac.uk/help/costs-terms-and-conditions>).

Offers tools for parsing and analyzing URL datasets, extracting key components and identifying common patterns. It aids in examining website architecture and identifying SEO issues, helping users optimize web presence and content strategy.

This package provides a comprehensive toolkit for quantum computing simulation and visualization within the R environment. The package enables users to initialize qubit states, construct custom quantum gates with both unitary transformation and visual parameters, and build full quantum circuits by sequentially adding gates. It includes predefined common gates (e.g., Hadamard, Pauli-X/Y/Z, Control-NOT, Control-Z) and supports direct plotting of circuits and individual gates for intuitive analysis.

Algorithms for checking the accuracy of a clustering result with known classes, computing cluster validity indices, and generating plots for comparing them. The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage). The details of the indices in this package can be found in: J. C. Bezdek, M. Moshtaghi, T. Runkler, C. Leckie (2016) <doi:10.1109/TFUZZ.2016.2540063>, T. Calinski, J. Harabasz (1974) <doi:10.1080/03610927408827101>, C. H. Chou, M. C. Su, E. Lai (2004) <doi:10.1007/s10044-004-0218-1>, D. L. Davies, D. W. Bouldin (1979) <doi:10.1109/TPAMI.1979.4766909>, J. C. Dunn (1973) <doi:10.1080/01969727308546046>, F. Haouas, Z. Ben Dhiaf, A. Hammouda, B. Solaiman (2017) <doi:10.1109/FUZZ-IEEE.2017.8015651>, M. Kim, R. S. Ramakrishna (2005) <doi:10.1016/j.patrec.2005.04.007>, S. H. Kwon (1998) <doi:10.1049/EL:19981523>, S. H. Kwon, J. Kim, S. H. Son (2021) <doi:10.1049/ell2.12249>, G. W. Miligan (1980) <doi:10.1007/BF02293907>, M. K. Pakhira, S. Bandyopadhyay, U. Maulik (2004) <doi:10.1016/j.patcog.2003.06.005>, M. Popescu, J. C. Bezdek, T. C. Havens, J. M. Keller (2013) <doi:10.1109/TSMCB.2012.2205679>, S. Saitta, B. Raphael, I. Smith (2007) <doi:10.1007/978-3-540-73499-4_14>, A. Starczewski (2017) <doi:10.1007/s10044-015-0525-8>, Y. Tang, F. Sun, Z. Sun (2005) <doi:10.1109/ACC.2005.1470111>, N. Wiroonsri (2024) <doi:10.1016/j.patcog.2023.109910>, N. Wiroonsri, O. Preedasawakul (2023) <doi:10.48550/arXiv.2308.14785>, C. H. Wu, C. S. Ouyang, L. W. Chen, L. W. Lu (2015) <doi:10.1109/TFUZZ.2014.2322495>, X. Xie, G. Beni (1991) <doi:10.1109/34.85677> and P.J. Rousseeuw (1987) and L. Kaufman and P.J. Rousseeuw(2009) <doi:10.1016/0377-0427(87)90125-7> and <doi:10.1002/9780470316801> C. Alok. (2010).

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.

Downloads data from the UK Police public data API, the full docs of which are available at <https://data.police.uk/docs/>. Includes data on police forces and police force areas, crime reports, and the use of stop-and-search powers.

Quickly create, run, and report structural equation models, and twin models. See ?umx for help, and umx_open_CRAN_page("umx") for NEWS. Timothy C. Bates, Michael C. Neale, Hermine H. Maes, (2019). umx: A library for Structural Equation and Twin Modelling in R. Twin Research and Human Genetics, 22, 27-41. <doi:10.1017/thg.2019.2>.

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.

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 framework for estimating difference-in-differences with unpoolable data, based on Karim, Webb, Austin, and Strumpf (2025) <doi:10.48550/arXiv.2403.15910>. Supports common or staggered adoption, multiple groups, and the inclusion of covariates. Also computes p-values for the aggregate average treatment effect on the treated via the randomization inference procedure described in MacKinnon and Webb (2020) <doi:10.1016/j.jeconom.2020.04.024>.

Testing whether two discrete variables have a functional relationship under null distributions where the two variables are statistically independent with fixed marginal counts. The fast enumeration algorithm was based on (Nguyen et al. 2020) <doi:10.24963/ijcai.2020/372>.

Historical voting data of the United Nations General Assembly. This includes votes for each country in each roll call, as well as descriptions and topic classifications for each vote.

Plots traced ultrasound tongue imaging data according to a polar coordinate system. There is currently support for plotting means and standard deviations of each category's trace; Smoothing Splines Analysis of Variance (SSANOVA) could be implemented as well. The origin of the polar coordinates may be defined manually or automatically determined based on different algorithms. Points for each category can be split into two groups (anterior and posterior) at the point of maximum curvature of each trace. User can specify rays to intersect various parts of the tongue; intersections along these rays serve as input for a pairwise t-test to measure significant contrasts between segments. Currently ultrapolaRplot supports ultrasound tongue imaging trace data from UltraTrace (<https://github.com/SwatPhonLab/UltraTrace>). UltraTrace is capable of importing data from Articulate Instruments AAA. read_textgrid.R is required for opening TextGrids to determine category and alignment information of ultrasound traces.

Extracts coordinates of an event location from text based on dictionaries of landmarks, roads, and areas. Only returns the location of an event of interest and ignores other location references; for example, if determining the location of a road traffic crash from the text "crash near [location 1] heading towards [location 2]", only the coordinates of "location 1" would be returned. Moreover, accounts for differences in spelling between how a user references a location and how a location is captured in location dictionaries. For more information on the algorithm, see Milusheva et al. (2021) <doi:10.1371/journal.pone.0244317>.

Wraps the unrtf utility <https://www.gnu.org/software/unrtf/> to extract text from RTF files. Supports document conversion to HTML, LaTeX or plain text. Output in HTML is recommended because unrtf has limited support for converting between character encodings.

This package provides a tool to define the rare biosphere. ulrb solves the problem of the definition of rarity by replacing arbitrary thresholds with an unsupervised machine learning algorithm (partitioning around medoids, or k-medoids). This algorithm works for any type of microbiome data, provided there is an abundance table. This method also works for non-microbiome data.

Uniform Error Index is the weighted average of different error measures. Uniform Error Index utilizes output from different error function and gives more robust and stable error values. This package has been developed to compute Uniform Error Index from ten different loss function like Error Square, Square of Square Error, Quasi Likelihood Error, LogR-Square, Absolute Error, Absolute Square Error etc. The weights are determined using Principal Component Analysis (PCA) algorithm of Yeasin and Paul (2024) <doi:10.1007/s11227-023-05542-3>.

Track the history of any R object and move through it with undo and redo operations. Anything can be stored, from a single number to a data frame or an entire application state. A manager can restrict its history to specific classes and cap how many items it keeps, and it can be reactive to integrate with Shiny'.

Retrieve data from the UNESCO Institute for Statistics (UIS) API <https://api.uis.unesco.org/api/public/documentation/>. UIS provides public access to more than 4,000 indicators focusing on education, science and technology, culture, and communication.

Elasticsearch is an open-source, distributed, document-based datastore (<https://www.elastic.co/products/elasticsearch>). It provides an HTTP API for querying the database and extracting datasets, but that API was not designed for common data science workflows like pulling large batches of records and normalizing those documents into a data frame that can be used as a training dataset for statistical models. uptasticsearch provides an interface for Elasticsearch that is explicitly designed to make these data science workflows easy and fun.

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.

Model data with a suspected clustering structure (either in co-variate space, regression space or both) using a Bayesian product model with a logistic regression likelihood. Observations are represented graphically and clusters are formed through various edge removals or additions. Cluster quality is assessed through the log Bayesian evidence of the overall model, which is estimated using either a Sequential Monte Carlo sampler or a suitable transformation of the Bayesian Information Criterion as a fast approximation of the former. The internal Iterated Batch Importance Sampling scheme (Chopin (2002 <doi:10.1093/biomet/89.3.539>)) is made available as a free standing function.

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.

Pseudo-random number generation of 17 univariate distributions proposed by Demirtas. (2005) <DOI:10.22237/jmasm/1114907220>.