It provides three implementations of hash tables and hash maps: 1. using std::unordered_map and std::unordered_set C++ libraries, 2. wrapping around the fastmatch package, 3. using R environment.

Maintenance has been discontinued for this package. It has been superseded by GeneralizedHyperbolic'. GeneralizedHyperbolic includes all the functionality of HyperbolicDist and more and is based on a more rational design. HyperbolicDist provides functions for the hyperbolic and related distributions. Density, distribution and quantile functions and random number generation are provided for the hyperbolic distribution, the generalized hyperbolic distribution, the generalized inverse Gaussian distribution and the skew-Laplace distribution. Additional functionality is provided for the hyperbolic distribution, including fitting of the hyperbolic to data.

Datasets related to Hong Kong, including information on the 2019 elected District Councillors (<https://www.districtcouncils.gov.hk> and <https://dce2019.hk01.com/>) and traffic collision data from the Hong Kong Department of Transport (<https://www.td.gov.hk/>). All of the data in this package is available in the public domain.

User-friendly and fast set of functions for estimating parameters of hierarchical Bayesian species distribution models (Latimer and others 2006 <doi:10.1890/04-0609>). Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results.

This package implements the Heterogeneous Transition Network Analysis (HTNA) method described by López-Pernas et al. (2026) <doi:10.1002/jcal.70285>. The method is an extension of transition network analysis (TNA) where actions or events belong to two or more distinct actor types (e.g. Human and AI), preserving the actor type partition on the resulting network. Provides a thin, focused API on top of the Nestimate estimation engine and the cograph rendering engine, so downstream bootstrap, permutation, reliability, centrality, and plotting functions treat each actor's codes as a distinct node group.

Estimate parameters of the hysteretic threshold autoregressive (HysTAR) model, using conditional least squares. In addition, you can generate time series data from the HysTAR model. For details, see Li, Guan, Li and Yu (2015) <doi:10.1093/biomet/asv017>.

Template R package with minimal setup to use Rust code in R without hacks or frameworks. Includes basic examples of importing cargo dependencies, spawning threads and passing numbers or strings from Rust to R. Cargo crates are automatically vendored in the R source package to support offline installation. The GitHub repository for this package has more details and also explains how to set up CI. This project was first presented at Erum2018 to showcase R-Rust integration <https://jeroen.github.io/erum2018/>; for a real world use-case, see the gifski package on CRAN'.

Access models and datasets hosted on the Hugging Face Hub through its Inference Application Programming Interface (API). Run text classification, embeddings, chat, translation, image, audio, and other tasks from tidy R workflows without installing Python by default. Results are returned as data frames or simple R objects so they can be composed with dplyr', tidyr', and related tooling. Helpers also support Hub search, file download, provider discovery, and guarded uploads for authenticated workflows. Optional local embeddings and text classification use Python through reticulate'.

Fits hidden Markov models with discrete non-parametric observation distributions to data sets. The observations may be univariate or bivariate. Simulates data from such models. Finds most probable underlying hidden states, the most probable sequences of such states, and the log likelihood of a collection of observations given the parameters of the model. Auxiliary predictors are accommodated in the univariate setting.

Import and classify canopy fish-eye images, estimate angular gap fraction and derive canopy attributes like leaf area index and openness. Additional information is provided in the study by Chianucci F., Macek M. (2023) <doi:10.1016/j.agrformet.2023.109470>.

Raster based flood modelling internally using hyd1d', an R package to interpolate 1d water level and gauging data. The package computes flood extent and duration through strategies originally developed for INFORM', an ArcGIS'-based hydro-ecological modelling framework. It does not provide a full, physical hydraulic modelling algorithm, but a simplified, near real time GIS approach for flood extent and duration modelling. Computationally demanding annual flood durations have been computed already and data products were published by Weber (2022) <doi:10.1594/PANGAEA.948042>.

This package provides a method for estimating the correlation matrix of the Gaussian copula from the observed data. This package also contains a penalized estimation of the corresponding precision matrix, and enables to generate random vectors that are distributed according to a Gaussian copula.

This package implements the simpler and faster heat index, which matches the values of the original 1979 heat index and its 2022 extension for air temperatures above 300 K (27 C, 80 F) and with only minor differences at lower temperatures. Also implements an algorithm for calculating the thermodynamic (and psychrometric) wet-bulb (and ice-bulb) temperature.

Work with model files (setup, input, output) from the hydrological catchment model HYPE: Streamlined file import and export, standard evaluation plot routines, diverse post-processing and aggregation routines for hydrological model analysis. The HYPEtools package is also archived at <doi:10.5281/zenodo.7627955> and can be cited in publications with Brendel et al. (2024) <doi:10.1016/j.envsoft.2024.106094>.

This package provides functions for the management and treatment of hydrology and meteorology time-series stored in a Sqlite data base.

Perform statistical writership analysis of scanned handwritten documents with a shiny app for handwriter'.

Tracks elapsed clock time using a `hms::hms()` scalar. It was was originally developed to time Bayesian model runs. It should not be used to estimate how long extremely fast code takes to execute as the package code adds a small time cost.

Discriminant analysis and data clustering methods for high dimensional data, based on the assumption that high-dimensional data live in different subspaces with low dimensionality proposing a new parametrization of the Gaussian mixture model which combines the ideas of dimension reduction and constraints on the model.

This package provides a set of R functions which implements Hotelling's T^2 test and some variants of it. Functions are also included for Aitchison's additive log ratio and centred log ratio transformations.

This package provides a user-friendly interface for the Hierarchical Data Format 5 ('HDF5') library designed to "just work." It bundles the necessary system libraries to ensure easy installation on all platforms. Features smart defaults that automatically map R objects (vectors, matrices, data frames) to efficient HDF5 types, removing the need to manage low-level details like dataspaces or property lists. Uses the HDF5 library developed by The HDF Group <https://www.hdfgroup.org/>.

This package provides a unified API for creating data visualizations across dynamic and static rendering modes. Visualizations are defined once using a specification object and can be rendered with highcharter', ggplot2', or other supported packages without modifying user code. The package supports declarative and layered workflows, reusable themes and colour palettes, optional JavaScript enhancements, export tools, and interactive exploration through shiny applications.

Events from individual hydrologic time series are extracted, and events are matched across multiple time series. The package has been applied in studies such as Wasko and Guo (2022) <doi:10.1002/hyp.14563> and Mohammadpour Khoie, Guo and Wasko (2025) <doi:10.1016/j.envsoft.2025.106521>.

Calculate Hopkins statistic to assess the clusterability of data. See Wright (2023) <doi:10.32614/RJ-2022-055>.

Hadamard matrix based statistical designs are of immense importance as the resultant designs carry various desirable characterizing properties. Constructing Partially Balanced Incomplete Block Designs (PBIBds) using Kronecker product of incidence matrices of Balanced Incomplete Block (BIB) and Partially Balanced Incomplete Block (PBIB) designs is much evident from literature. Here, we have constructed Incomplete Block Designs (IBDs) based on Hadamard matrices and Kronecker product of Hadamard matrices.