The half-weight index gregariousness (HWIG) is an association index used in social network analyses. It extends the half-weight association index (HWI), correcting for level of gregariousness in individuals. It is calculated using group by individual data according to methods described in Godde et al. (2013) <doi:10.1016/j.anbehav.2012.12.010>.

This package provides a lightweight framework for building server-driven web applications in R'. htmxr combines the simplicity of htmx for partial page updates with the power of plumber2 for non-blocking HTTP endpoints. Build interactive dashboards and data applications without writing JavaScript', using familiar R patterns inspired by Shiny'. For more information on htmx', see <https://htmx.org>.

Compute 21 summary measures of health inequality and its corresponding confidence intervals for ordered and non-ordered dimensions using disaggregated data. Measures for ordered dimensions (e.g., Slope Index of Inequality, Absolute Concentration Index) also accept individual and survey data.

Homomorphic computations in R for privacy-preserving applications. Currently only the Paillier Scheme is implemented.

This package provides a generic function and a set of methods to calculate highest density intervals for a variety of classes of objects which can specify a probability density distribution, including MCMC output, fitted density objects, and functions.

This package provides functions to access data from the US Department of Housing and Urban Development <https://www.huduser.gov/portal/dataset/fmr-api.html>.

This package provides functions to build and use hexagonal discrete global grids using the Snyder ISEA projection ('Snyder 1992 <doi:10.3138/27H7-8K88-4882-1752>) and the H3 hierarchical hexagonal system ('Uber Technologies). Implements the ISEA discrete global grid system ('Sahr', White and Kimerling 2003 <doi:10.1559/152304003100011090>). Includes a fast C++ core for ISEA projection and aperture quantization, an included H3 v4.4.1 C library for native H3 grid operations, and sf'/'terra'-compatible R wrappers for grid generation and coordinate assignment. Output is compatible with dggridR for interoperability.

This package provides a histogram slider input binding for use in Shiny'. Currently supports creating histograms from numeric, date, and date-time vectors.

An open-source R package to deploys reproducible and flexible labels using layers. The huito package is part of the inkaverse project for developing different procedures and tools used in plant science and experimental designs. Learn more about the inkaverse project at <https://inkaverse.com/>.

This package provides a handy collection of utility functions designed to aid in package development, plotting and scientific research. Package development functionalities includes among others tools such as cross-referencing package imports with the description file, analysis of redundant package imports, editing of the description file and the creation of package badges for GitHub. Some of the other functionalities include automatic package installation and loading, plotting points without overlap, creating nice breaks for plots, overview tables and many more handy utility functions.

High level functions for hyperplane fitting (hyper.fit()) and visualising (hyper.plot2d() / hyper.plot3d()). In simple terms this allows the user to produce robust 1D linear fits for 2D x vs y type data, and robust 2D plane fits to 3D x vs y vs z type data. This hyperplane fitting works generically for any N-1 hyperplane model being fit to a N dimension dataset. All fits include intrinsic scatter in the generative model orthogonal to the hyperplane.

In some cases you will have data in a histogram format, where you have a vector of all possible observations, and a vector of how many times each observation appeared. You could expand this into a single 1D vector, but this may not be advisable if the counts are extremely large. HistDat allows for the calculation of summary statistics without the need for expanding your data.

Estimation of high-dimensional multi-response regression with heterogeneous noises under Heterogeneous group square-root Lasso penalty. For details see: Ren, Z., Kang, Y., Fan, Y. and Lv, J. (2018)<arXiv:1606.03803>.

Spatial heterogeneity can be specified in various ways. hspm is an ambitious project that aims at implementing various methodologies to control for heterogeneity in spatial models. The current version of hspm deals with spatial and (non-spatial) regimes models. In particular, the package allows to estimate a general spatial regimes model with additional endogenous variables, specified in terms of a spatial lag of the dependent variable, the spatially lagged regressors, and, potentially, a spatially autocorrelated error term. Spatial regime models are estimated by instrumental variables and generalized methods of moments (see Arraiz et al., (2010) <doi:10.1111/j.1467-9787.2009.00618.x>, Bivand and Piras, (2015) <doi:10.18637/jss.v063.i18>, Drukker et al., (2013) <doi:10.1080/07474938.2013.741020>, Kelejian and Prucha, (2010) <doi:10.1016/j.jeconom.2009.10.025>).

Nonparametric cumulative-incidence based estimation of the ratios of sub-hazard ratios to cause-specific hazard ratios using the approach from Ng et al. (2020).

Historical borrowing in clinical trials can improve precision and operating characteristics. This package supports a longitudinal hierarchical model to borrow historical control data from other studies to better characterize the control response of the current study. It also quantifies the amount of borrowing through longitudinal benchmark models (independent and pooled). The hierarchical model approach to historical borrowing is discussed by Viele et al. (2013) <doi:10.1002/pst.1589>.

This package provides a unified, extensible interface to discover hydrologic stations and download daily time series (e.g., water discharge, water level, water temperature, and several other water quality parameter) from national and regional public APIs. Includes a provider registry, S3 generics stations and timeseries', licensing metadata, date-range and complete history modes, rate limiting and retries, optional authentication via environment variables, tidy outputs, UTF-8 to ASCII transliteration, and WGS84 coordinates. Designed for reproducible workflows and straightforward addition of new providers. Background and use cases are described in Farber et al. (2025) <doi:10.5194/essd-17-4613-2025> and Farber et al. (2023) <doi:10.57757/IUGG23-2838>.

An implementation of the nonnegative garrote method that incorporates hierarchical relationships among variables. The core function, HiGarrote(), offers an automated approach for analyzing experiments while respecting hierarchical structures among effects. For methodological details, refer to Yu and Joseph (2025) <doi:10.1080/00224065.2025.2513508>. This work is supported by U.S. National Science Foundation grant DMS-2310637.

This package implements assessment of benefit-risk balance using Bayesian Discrete Choice Experiment. For more details see the article by Mukhopadhyay et al. (2019) <DOI:10.1080/19466315.2018.1527248>.

This package provides a Shiny app allowing to convert HTML code to R code (e.g. <span>Hello</span> to tags$span("Hello")'), for usage in a Shiny UI.

Returns a Hasse diagram of the layout structure (Bate and Chatfield (2016)) <doi:10.1080/00224065.2016.11918173> or the restricted layout structure (Bate and Chatfield (2016)) <doi:10.1080/00224065.2016.11918174> of an experimental design.

This package contains data for software hotspot analysis, along with a function performing the analysis itself.

Builds and optimizes Hopfield artificial neural networks (Hopfield, 1982, <doi:10.1073/pnas.79.8.2554>). One-layer and three-layer models are implemented. The energy of the Hopfield network is minimized with formula from Krotov and Hopfield (2016, <doi:10.48550/ARXIV.1606.01164>). Optimization (supervised learning) is done through a gradient-based method. Classification is done with S3 methods predict(). Parallelization with OpenMP is used if available during compilation.

Generates Hadamard matrices using different construction methods. For those who want to generate Hadamard matrix, a generic function, Hadamard_matrix() is provided. For those who want to generate Hadamard matrix using a particular method, separate functions are available. See Horadam (2007, ISBN:9780691119212) Hadamard Matrices and their applications, Princeton University Press for more information on Hadamard Matrices.