Fit, summarize and plot sinusoidal hysteretic processes using: two-step simple harmonic least squares, ellipse-specific non-linear least squares, the direct method, geometric least squares or linear least squares. See Yang, F and A. Parkhurst, "Efficient Estimation of Elliptical Hysteresis with Application to the Characterization of Heat Stress" <DOI:10.1007/s13253-015-0213-6>.

When considering count data, it is often the case that many more zero counts than would be expected of some given distribution are observed. It is well established that data such as this can be reliably modelled using zero-inflated or hurdle distributions, both of which may be applied using the functions in this package. Bayesian analysis methods are used to best model problematic count data that cannot be fit to any typical distribution. The package functions are flexible and versatile, and can be applied to varying count distributions, parameter estimation with or without explanatory variable information, and are able to allow for multiple hurdles as it is also not uncommon that count data have an abundance of large-number observations which would be considered outliers of the typical distribution. In lieu of throwing out data or misspecifying the typical distribution, these extreme observations can be applied to a second, extreme distribution. With the given functions of this package, such a two-hurdle model may be easily specified in order to best manage data that is both zero-inflated and over-dispersed.

An implementation for high-dimensional time series analysis methods, including factor model for vector time series proposed by Lam and Yao (2012) <doi:10.1214/12-AOS970> and Chang, Guo and Yao (2015) <doi:10.1016/j.jeconom.2015.03.024>, martingale difference test proposed by Chang, Jiang and Shao (2023) <doi:10.1016/j.jeconom.2022.09.001>, principal component analysis for vector time series proposed by Chang, Guo and Yao (2018) <doi:10.1214/17-AOS1613>, cointegration analysis proposed by Zhang, Robinson and Yao (2019) <doi:10.1080/01621459.2018.1458620>, unit root test proposed by Chang, Cheng and Yao (2022) <doi:10.1093/biomet/asab034>, white noise test proposed by Chang, Yao and Zhou (2017) <doi:10.1093/biomet/asw066>, CP-decomposition for matrix time series proposed by Chang et al. (2023) <doi:10.1093/jrsssb/qkac011> and Chang et al. (2024) <doi:10.48550/arXiv.2410.05634>, and statistical inference for spectral density matrix proposed by Chang et al. (2022) <doi:10.48550/arXiv.2212.13686>.

This package provides functionality to download and cache files from Hugging Face Hub <https://huggingface.co/models>. Uses the same caching structure so files can be shared between different client libraries.

This package provides a method for identifying responses to experimental stimulation in mass or flow cytometry that uses high dimensional analysis of measured parameters and can be performed with an end-to-end unsupervised approach. In the context of in vitro stimulation assays where high-parameter cytometry was used to monitor intracellular response markers, using cell populations annotated either through automated clustering or manual gating for a combined set of stimulated and unstimulated samples, HDStIM labels cells as responding or non-responding. The package also provides auxiliary functions to rank intracellular markers based on their contribution to identifying responses and generating diagnostic plots.

Identifies chromatin interaction modules by constructing a Hi-C contact network based on statistically significant interactions, followed by network clustering. The method enables comparison of module connectivity across two Hi-C datasets and is capable of detecting cell-type-specific regulatory modules. By integrating network analysis with chromatin conformation data, this approach provides insights into the spatial organization of the genome and its functional implications in gene regulation. Author: Sora Yoon (2025) <https://github.com/ysora/HiCociety>.

H-index and h-alpha are a bibliometric indicators. This package provides functions to simulate how these indicators may develop over time for a given set of researchers and to visualize the simulation data. The implementation is based on the STATA ado h-index and is described in more detail in Bornmann et al. (2019) <arXiv:1905.11052>.

These sample data sets are intended for historians learning R. They include population, institutional, religious, military, and prosopographical data suitable for mapping, quantitative analysis, and network analysis.

Fits regression models on high dimensional data to estimate coefficients and use bootstrap method to obtain confidence intervals. Choices for regression models are Lasso, Lasso+OLS, Lasso partial ridge, Lasso+OLS partial ridge.

This package provides a broad collection of datasets focused on health, biomechanics, and human motion. It includes clinical, physiological, and kinematic information from diverse sources, covering aspects such as surgery outcomes, vital signs, rheumatoid arthritis, osteoarthritis, accelerometry, gait analysis, motion sensing, and biomechanics experiments. Designed for researchers, analysts, and students, the package facilitates exploration and analysis of data related to health monitoring, physical activity, and rehabilitation.

We present this package for fitting structural equation models using the hierarchical likelihood method. This package allows extended structural equation model, including dynamic structural equation model. We illustrate the use of our packages with well-known data sets. Therefore, this package are able to handle two serious problems inadmissible solution and factor indeterminacy <doi:10.3390/sym13040657>.

This package provides a way to display word clouds in R. The word cloud is a html widget, so you can use it in interactive documents and shiny applications.

Package provides the estimation of the structure and the parameters, sampling methods and structural plots of Hierarchical Archimedean Copulae (HAC).

Meyer and Held (2017) <doi:10.1093/biostatistics/kxw051> present an age-structured spatio-temporal model for infectious disease counts. The approach is illustrated in a case study on norovirus gastroenteritis in Berlin, 2011-2015, by age group, city district and week, using additional contact data from the POLYMOD survey. This package contains the data and code to reproduce the results from the paper, see demo("hhh4contacts")'.

This tool identifies hydropeaking events from raw time-series flow record, a rapid flow variation induced by the hourly-adjusted electricity market. The novelty of HEDA is to use vector angle instead of the first-order derivative to detect change points which not only largely improves the computing efficiency but also accounts for the rate of change of the flow variation. More details <doi:10.1016/j.jhydrol.2021.126392>.

Homomorphic encryption (Brakerski and Vaikuntanathan (2014) <doi:10.1137/120868669>) using Ring Learning with Errors (Lyubashevsky et al. (2012) <https://eprint.iacr.org/2012/230>) is a form of Learning with Errors (Regev (2005) <doi:10.1145/1060590.1060603>) using polynomial rings over finite fields. Functions to generate the required polynomials (using polynom'), with various distributions of coefficients are provided. Additionally, functions to generate and take coefficient modulo are provided.

S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models. Missing values in observed and/or simulated values can be removed before computations. Comments / questions / collaboration of any kind are very welcomed.

Linear and logistic regression models penalized with hierarchical shrinkage priors for selection of biomarkers (or more general variable selection), which can be fitted using Stan (Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>). It implements the horseshoe and regularized horseshoe priors (Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>), as well as the projection predictive selection approach to recover a sparse set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020) <doi:10.1214/20-EJS1711>).

This package implements the high-dimensional two-sample test proposed by Zhang (2019) <http://hdl.handle.net/2097/40235>. It also implements the test proposed by Srivastava, Katayama, and Kano (2013) <doi:10.1016/j.jmva.2012.08.014>. These tests are particularly suitable to high dimensional data from two populations for which the classical multivariate Hotelling's T-square test fails due to sample sizes smaller than dimensionality. In this case, the ZWL and ZWLm tests proposed by Zhang (2019) <http://hdl.handle.net/2097/40235>, referred to as zwl_test() in this package, provide a reliable and powerful test.

Audio interactivity within shiny applications using howler.js'. Enables the status of the audio player to be sent from the UI to the server, and events such as playing and pausing the audio can be triggered from the server.

Health Calculator helps to find different parameters like basal metabolic rate, body mass index etc. related to fitness and health of a person.

This package implements various heuristics like Take The Best and unit-weight linear, which do two-alternative choice: which of two objects will have a higher criterion? Also offers functions to assess performance, e.g. percent correct across all row pairs in a data set and finding row pairs where models disagree. New models can be added by implementing a fit and predict function-- see vignette. Take The Best was first described in: Gigerenzer, G. & Goldstein, D. G. (1996) <doi:10.1037/0033-295X.103.4.650>. All of these heuristics were run on many data sets and analyzed in: Gigerenzer, G., Todd, P. M., & the ABC Group (1999). <ISBN:978-0195143812>.

Vapor pressure, relative humidity, absolute humidity, specific humidity, and mixing ratio are commonly used water vapor measures in meteorology. This R package provides functions for calculating saturation vapor pressure (hPa), partial water vapor pressure (Pa), relative humidity (%), absolute humidity (kg/m^3), specific humidity (kg/kg), and mixing ratio (kg/kg) from temperature (K) and dew point (K). Conversion functions between humidity measures are also provided.

Focuses on data processing and visualization in hydrology and climate forecasting. Main function includes data extraction, data downscaling, data resampling, gap filler of precipitation, bias correction of forecasting data, flexible time series plot, and spatial map generation. It is a good pre- processing and post-processing tool for hydrological and hydraulic modellers.