This package provides a self-contained, static build of the HDF5 (Hierarchical Data Format 5) C library (release 2.1.1) for R package developers. Designed for use in the LinkingTo field, it enables zero-dependency integration by building the library entirely from source during installation. Additionally, it compiles and internally links a comprehensive suite of advanced compression filters and their HDF5 plugins (Zstd, LZ4, Blosc/Blosc2, Snappy, ZFP, Bzip2, LZF, Bitshuffle, szip, and gzip). These plugins are integrated out-of-the-box, allowing downstream packages to utilize high-performance compression directly through the standard HDF5 API while keeping the underlying third-party headers fully encapsulated. HDF5 is developed by The HDF Group <https://www.hdfgroup.org/>.

This package provides functions for converting local HTML and XHTML files to PDF using an external backend. The package includes helper functions for backend setup, file conversion, and an optional Tk graphical interface.

An interactive Shiny dashboard for visualizing and exploring key metrics related to HIV/AIDS, including prevalence, incidence, mortality, and treatment coverage. The dashboard is designed to work with a dataset containing specific columns with standardized names. These columns must be present in the input data for the app to function properly: year: Numeric year of the data (e.g. 2010, 2021); sex: Gender classification (e.g. Male, Female); age_group: Age bracket (e.g. 15â 24, 25â 34); hiv_prevalence: Estimated HIV prevalence percentage; hiv_incidence: Number of new HIV cases per year; aids_deaths: Total AIDS-related deaths; plhiv: Estimated number of people living with HIV; art_coverage: Percentage receiving antiretroviral therapy (ART); testing_coverage: HIV testing services coverage; causes: Description of likely HIV transmission cause (e.g. unprotected sex, drug use). The dataset structure must strictly follow this column naming convention for the dashboard to render correctly.

Tests for two high-dimensional population mean vectors. The user has the option to compute the asymptotic, the permutation or the bootstrap based p-value of the test. Some references are: Chen S.X. and Qin Y.L. (2010). <doi:10.1214/09-AOS716>, Cai T.T., Liu W., and Xia Y. (2014) <doi:10.1111/rssb.12034> and Yu X., Li D., Xue L. and Li, R. (2023) <doi:10.1080/01621459.2022.2061354>.

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 shiny interface for a free, open-source managerial accounting-like system for health care practices. This package allows health care administrators to project revenue with monthly adjustments and procedure-specific boosts up to a 3-year period. Granular data (patient-level) to aggregated data (department- or hospital-level) can all be used as valid inputs provided historical volume and revenue data is available. For more details on managerial accounting techniques, see Brewer et al. (2015, ISBN:9780078025792).

General (multi-allelic) Hardy-Weinberg equilibrium problem from an objective Bayesian testing standpoint. This aim is achieved through the identification of a class of priors specifically designed for this testing problem. A class of intrinsic priors under the full model is considered. This class is indexed by a tuning quantity, the training sample size, as discussed in Consonni, Moreno and Venturini (2010). These priors are objective, satisfy Savage's continuity condition and have proved to behave extremely well for many statistical testing problems.

Allows for painless use of the Metopio health atlas APIs <https://metopio.com/health-atlas> to explore and import data. Metopio health atlases store open public health data. See what topics (or indicators) are available among specific populations, periods, and geographic layers. Download relevant data along with geographic boundaries or point datasets. Spatial datasets are returned as sf objects.

This package provides univariate and indexed (multivariate) nonparametric smoothed kernel estimators for the future conditional hazard rate function when time-dependent covariates are present, a bandwidth selector for the estimator's implementation and pointwise and uniform confidence bands. Methods used in the package refer to Bagkavos, Isakson, Mammen, Nielsen and Proust-Lima (2025) <doi:10.1093/biomet/asaf008>.

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.

HIGHT(HIGh security and light weigHT) algorithm is a block cipher encryption algorithm developed to provide confidentiality in computing environments that demand low power consumption and lightweight, such as RFID(Radio-Frequency Identification) and USN(Ubiquitous Sensor Network), or in mobile environments that require low power consumption and lightweight, such as smartphones and smart cards. Additionally, it is designed with a simple structure that enables it to be used with basic arithmetic operations, XOR, and circular shifts in 8-bit units. This algorithm was designed to consider both safety and efficiency in a very simple structure suitable for limited environments, compared to the former 128-bit encryption algorithm SEED. In December 2010, it became an ISO(International Organization for Standardization) standard. The detailed procedure is described in Hong et al. (2006) <doi:10.1007/11894063_4>.

Core set of low-level utilities common across the hubverse'. Used to interact with hubverse schema, Hub configuration files and model outputs and designed to be primarily used internally by other hubverse packages. See Reich et al. (2022) <doi:10.2105/AJPH.2022.306831> for an overview of Collaborative Hubs.

This package provides access to Uber's H3 library for geospatial indexing via its JavaScript transpile h3-js <https://github.com/uber/h3-js> and V8 <https://github.com/jeroen/v8>.

This package provides a user-friendly tool to fit Bayesian regression models. It can fit 3 types of Bayesian models using individual-level, summary-level, and individual plus pedigree-level (single-step) data for both Genomic prediction/selection (GS) and Genome-Wide Association Study (GWAS), it was designed to estimate joint effects and genetic parameters for a complex trait, including: (1) fixed effects and coefficients of covariates, (2) environmental random effects, and its corresponding variance, (3) genetic variance, (4) residual variance, (5) heritability, (6) genomic estimated breeding values (GEBV) for both genotyped and non-genotyped individuals, (7) SNP effect size, (8) phenotype/genetic variance explained (PVE) for single or multiple SNPs, (9) posterior probability of association of the genomic window (WPPA), (10) posterior inclusive probability (PIP). The functions are not limited, we will keep on going in enriching it with more features. References: Lilin Yin et al. (2025) <doi:10.18637/jss.v114.i06>; Meuwissen et al. (2001) <doi:10.1093/genetics/157.4.1819>; Gustavo et al. (2013) <doi:10.1534/genetics.112.143313>; Habier et al. (2011) <doi:10.1186/1471-2105-12-186>; Yi et al. (2008) <doi:10.1534/genetics.107.085589>; Zhou et al. (2013) <doi:10.1371/journal.pgen.1003264>; Moser et al. (2015) <doi:10.1371/journal.pgen.1004969>; Lloyd-Jones et al. (2019) <doi:10.1038/s41467-019-12653-0>; Henderson (1976) <doi:10.2307/2529339>; Fernando et al. (2014) <doi:10.1186/1297-9686-46-50>.

This package provides functions for designing phase II clinical trials adjusting for the heterogeneity of the population using known subgroups or historical controls.

Efficient Bayesian multinomial logistic regression based on heavy-tailed (hyper-LASSO, non-convex) priors. The posterior of coefficients and hyper-parameters is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed description of the method: Li and Yao (2018), Journal of Statistical Computation and Simulation, 88:14, 2827-2851, <doi:10.48550/arXiv.1405.3319>.

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.

Create publication-quality, 2-dimensional visualizations of alpha-helical peptide sequences. Specifically, allows the user to programmatically generate helical wheels and wenxiang diagrams to provide a bird's eye, top-down view of alpha-helical oligopeptides. See Wadhwa RR, et al. (2018) <doi:10.21105/joss.01008> for more information.

Perform statistical writership analysis of scanned handwritten documents. Webpage provided at: <https://github.com/CSAFE-ISU/handwriter>.

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.

Convert Chinese characters into Hanyu Pinyin (the official romanization system for Standard Chinese) with support for tones, toneless output, initials, URL slugs, and valid R variable names. The package was inspired by the now-orphaned CRAN package pinyin (archived in April 2026 after the maintainer became unreachable). hanyupinyin is a ground-up rewrite using the authoritative Unicode Unihan database, a vectorized engine, and modern R practices. Dictionary data are derived from the Unicode Unihan Database (Unicode Consortium, 2025) <https://www.unicode.org/reports/tr38/>.

This package provides functions for combining model outputs (e.g. predictions or estimates) from multiple models into an aggregated ensemble model output.

By binding R functions and the Highmaps <https://www.highcharts.com.cn/products/highmaps> chart library, hchinamap package provides a simple way to map China and its provinces. The map of China drawn by this package contains complete Chinese territory, especially the Nine-dotted line, South Tibet, Hong Kong, Macao and Taiwan.

Holistic Multimodel Domain Analysis (HMDA) is a robust and transparent framework designed for exploratory machine learning research, aiming to enhance the process of feature assessment and selection. HMDA addresses key limitations of traditional machine learning methods by evaluating the consistency across multiple high-performing models within a fine-tuned modeling grid, thereby improving the interpretability and reliability of feature importance assessments. Specifically, it computes Weighted Mean SHapley Additive exPlanations (WMSHAP), which aggregate feature contributions from multiple models based on weighted performance metrics. HMDA also provides confidence intervals to demonstrate the stability of these feature importance estimates. This framework is particularly beneficial for analyzing complex, multidimensional datasets common in health research, supporting reliable exploration of mental health outcomes such as suicidal ideation, suicide attempts, and other psychological conditions. Additionally, HMDA includes automated procedures for feature selection based on WMSHAP ratios and performs dimension reduction analyses to identify underlying structures among features. For more details see Haghish (2025) <doi:10.13140/RG.2.2.32473.63846>.