Simulates stochastic hybrid models for transmission of infectious diseases in dynamic networks. It is a metapopulation model in which each node in the network is a sub-population and disease spreads within nodes and among them, combining two approaches: stochastic simulation algorithm (<doi:10.1146/annurev.physchem.58.032806.104637>) and individual-based approach, respectively. Equations that models spread within nodes are customizable and there are two link types among nodes: migration and influence (commuting). More information in Fernando S. Marques, Jose H. H. Grisi-Filho, Marcos Amaku et al. (2020) <doi:10.18637/jss.v094.i06>.

Build better balance in causal inference models. halfmoon helps you assess propensity score models for balance between groups using metrics like standardized mean differences and visualization techniques like mirrored histograms. halfmoon supports both weighting and matching techniques.

H(x) is the h-index for the past x years. Here, the h(x) of a scientist/department/etc. can be calculated using the exported excel file from a Web of Science citation report of a search. Also calculated is the year of first publication, total number of publications, and sum of times cited for the specified period. Therefore, for h-10: the date of first publication, total number of publications, and sum of times cited in the past 10 years are calculated. Note: the excel file has to first be saved in a .csv format.

Estimates parameters in Mixture Transition Distribution (MTD) models, a class of high-order Markov chains. The set of relevant pasts (lags) is selected using either the Bayesian Information Criterion or the Forward Stepwise and Cut algorithms. Other model parameters (e.g. transition probabilities and oscillations) can be estimated via maximum likelihood estimation or the Expectation-Maximization algorithm. Additionally, hdMTD includes a perfect sampling algorithm that generates samples of an MTD model from its invariant distribution. For theory, see Ost & Takahashi (2023) <http://jmlr.org/papers/v24/22-0266.html>.

The heterogeneous multi-task feature learning is a data integration method to conduct joint feature selection across multiple related data sets with different distributions. The algorithm can combine different types of learning tasks, including linear regression, Huber regression, adaptive Huber, and logistic regression. The modified version of Bayesian Information Criterion (BIC) is produced to measure the model performance. Package is based on Yuan Zhong, Wei Xu, and Xin Gao (2022) <https://www.fields.utoronto.ca/talk-media/1/53/65/slides.pdf>.

This package provides two functions that implement the one-sided and two-sided versions of the Hodrick-Prescott filter. The one-sided version is a Kalman filter-based implementation, whereas the two- sided version uses sparse matrices for improved efficiency. References: Hodrick, R. J., and Prescott, E. C. (1997) <doi:10.2307/2953682> Mcelroy, T. (2008) <doi:10.1111/j.1368-423X.2008.00230.x> Meyer-Gohde, A. (2010) <https://ideas.repec.org/c/dge/qmrbcd/181.html> For more references, see the vignette.

This package provides a set of tools to create georeferenced hillshade relief raster maps using ray-tracing and other advanced hill-shading techniques. It includes a wrapper function to create a georeferenced, ray-traced hillshade map from a digital elevation model, and other functions that can be used in a rayshader pipeline.

This is a collection of functions for converting coordinates between WGS84UTM, WGS84GEO, HK80UTM, HK80GEO and HK1980GRID Coordinate Systems used in Hong Kong SAR, based on the algorithms described in Explanatory Notes on Geodetic Datums in Hong Kong by Survey and Mapping Office Lands Department, Hong Kong Government (1995).

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.

Generates valid HTML tag strings for HTML5 elements documented by Mozilla. Attributes are passed as named lists, with names being the attribute name and values being the attribute value. Attribute values are automatically double-quoted. To declare a DOCTYPE, wrap html() with function doctype(). Mozilla's documentation for HTML5 is available here: <https://developer.mozilla.org/en-US/docs/Web/HTML/Element>. Elements marked as obsolete are not included.

This package provides a procedure that fits derivative curves based on a sequence of quotient differences. In a hierarchical setting the package produces estimates of subject-specific and group-specific derivative curves. In a non-hierarchical setting the package produces a single derivative curve.

This package provides a comprehensive R package for accessing and working with publicly available and free resources from the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project (HCUP). The package provides streamlined access to HCUP's Clinical Classifications Software Refined (CCSR) mapping files and Summary Trend Tables, enabling researchers and analysts to efficiently map ICD-10-CM diagnosis codes and ICD-10-PCS procedure codes to CCSR categories and access HCUP statistical reports. Key features include: direct download from HCUP website, multiple output formats (long/wide/default), cross-classification support, version management, citation generation, and intelligent caching. The package does not redistribute HCUP data files but facilitates direct download from the official HCUP website, ensuring users always have access to the latest versions and maintain compliance with HCUP data use policies. This package only accesses free public tools and reports; it does NOT access HCUP databases (NIS, KID, SID, NEDS, etc.) that require purchase. For more information, see <https://hcup-us.ahrq.gov/>.

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.

This package performs genetic association analyses of case-parent triad (trio) data with multiple markers. It can also incorporate complete or incomplete control triads, for instance independent control children. Estimation is based on haplotypes, for instance SNP haplotypes, even though phase is not known from the genetic data. Haplin estimates relative risk (RR + conf.int.) and p-value associated with each haplotype. It uses maximum likelihood estimation to make optimal use of data from triads with missing genotypic data, for instance if some SNPs has not been typed for some individuals. Haplin also allows estimation of effects of maternal haplotypes and parent-of-origin effects, particularly appropriate in perinatal epidemiology. Haplin allows special models, like X-inactivation, to be fitted on the X-chromosome. A GxE analysis allows testing interactions between environment and all estimated genetic effects. The models were originally described in "Gjessing HK and Lie RT. Case-parent triads: Estimating single- and double-dose effects of fetal and maternal disease gene haplotypes. Annals of Human Genetics (2006) 70, pp. 382-396".

In high-dimensional settings: Estimate the number of distant spikes based on the Generalized Spiked Population (GSP) model. Estimate the population eigenvalues, angles between the sample and population eigenvectors, correlations between the sample and population PC scores, and the asymptotic shrinkage factors. Adjust the shrinkage bias in the predicted PC scores. Dey, R. and Lee, S. (2019) <doi:10.1016/j.jmva.2019.02.007>.

Identifies regime changes in streamflow runoff not explained by variations in precipitation. The package builds a flexible set of Hidden Markov Models of annual, seasonal or monthly streamflow runoff with precipitation as a predictor. Suites of models can be built for a single site, ranging from one to three states and each with differing combinations of error models and auto-correlation terms. The most parsimonious model is easily identified by AIC, and useful for understanding catchment drought non-recovery: Peterson TJ, Saft M, Peel MC & John A (2021) <doi:10.1126/science.abd5085>.

This package provides interpretable high-dimensional mean comparison methods (HMC). For example, users can apply these methods to assess the difference in gene expression between two treatment groups. It is not a gene-by-gene comparison. Instead, the methods focus on the interplay between features and identify those that are predictive of the group label. The tests are valid frequentist procedures and yield sparse estimates indicating which features contribute to the group differences.

Enhances the H2O platform by providing tools for detailed evaluation of machine learning models. It includes functions for bootstrapped performance evaluation, extended F-score calculations, and various other metrics, aimed at improving model assessment.

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.

We provide a stage-wise selection method using genetic algorithms, designed to efficiently identify main and two-way interactions within high-dimensional linear regression models. Additionally, it implements simulated annealing algorithm during the mutation process. The relevant paper can be found at: Ye, C.,and Yang,Y. (2019) <doi:10.1109/TIT.2019.2913417>.

High throughput toxicokinetics ("HTTK") is the combination of 1) chemical-specific in vitro measurements or in silico predictions and 2) generic mathematical models, to predict absorption, distribution, metabolism, and excretion by the body. HTTK methods have been described by Pearce et al. (2017) (<doi:10.18637/jss.v079.i04>) and Breen et al. (2021) (<doi:10.1080/17425255.2021.1935867>). Here we provide examples (vignettes) applying HTTK to solve various problems in bioinformatics, toxicology, and exposure science. In accordance with Davidson-Fritz et al. (2025) (<doi:10.1371/journal.pone.0321321>), whenever a new HTTK model is developed, the code to generate the figures evaluating that model is added as a new vignettte.

Hedgehog will eat all your bugs. Hedgehog is a property-based testing package in the spirit of QuickCheck'. With Hedgehog', one can test properties of their programs against randomly generated input, providing far superior test coverage compared to unit testing. One of the key benefits of Hedgehog is integrated shrinking of counterexamples, which allows one to quickly find the cause of bugs, given salient examples when incorrect behaviour occurs.

Format quantities of time or bytes into human-friendly strings.

Implementation of the Hysteretic and Gatekeeping Depressions Model (HGDM) which calculates variable connected/contributing areas and resulting discharge volumes in prairie basins dominated by depressions ("slough" or "potholes"). The small depressions are combined into a single "meta" depression which explicitly models the hysteresis between the storage of water and the connected/contributing areas of the depressions. The largest (greater than 5% of the total depressional area) depression (if it exists) is represented separately to model its gatekeeping, i.e. the blocking of upstream flows until it is filled. The methodolgy is described in detail in Shook and Pomeroy (2025, <doi:10.1016/j.jhydrol.2025.132821>).