Conducts analyses for healthcare program evaluations or intervention studies. Calculates regression analyses for standard ordinary least squares (OLS or linear) or logistic models. Performs regression models used for causal modeling such as differences-in-differences (DID) and interrupted time series (ITS) models. Provides limited interpretations of model results and a ranking of variable importance in models. Performs propensity score models, top-coding of model outcome variables, and can return new data with the newly formed variables. Also performs Cronbach's alpha for various scale items (e.g., survey questions). See Github URL for examples in the README file. For more details on the statistical methods, see Allen & Yen (1979, ISBN:0-8185-0283-5), Angrist & Pischke (2009, ISBN:9780691120355), Harrell (2016, ISBN:978-3-319-19424-0), Kline (1999, ISBN:9780415211581), Linden (2015) <doi:10.1177/1536867X1501500208>, Merlo (2006) <doi:10.1136/jech.2004.029454> Muthen & Satorra (1995) <doi:10.2307/271070>, and Rabe-Hesketh & Skrondal (2008, ISBN:978-1-59718-040-5).

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.

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.

The harmonic mean p-value (HMP) test combines p-values and corrects for multiple testing while controlling the strong-sense family-wise error rate. It is more powerful than common alternatives including Bonferroni and Simes procedures when combining large proportions of all the p-values, at the cost of slightly lower power when combining small proportions of all the p-values. It is more stringent than controlling the false discovery rate, and possesses theoretical robustness to positive correlations between tests and unequal weights. It is a multi-level test in the sense that a superset of one or more significant tests is certain to be significant and conversely when the superset is non-significant, the constituent tests are certain to be non-significant. It is based on MAMML (model averaging by mean maximum likelihood), a frequentist analogue to Bayesian model averaging, and is theoretically grounded in generalized central limit theorem. For detailed examples type vignette("harmonicmeanp") after installation. Version 3.0 addresses errors in versions 1.0 and 2.0 that led function p.hmp to control the familywise error rate only in the weak sense, rather than the strong sense as intended.

Convert a html document to plain texts by stripping off all html tags.

Harmony is a tool using AI which allows you to compare items from questionnaires and identify similar content. You can try Harmony at <https://harmonydata.ac.uk/app/> and you can read our blog at <https://harmonydata.ac.uk/blog/> or at <https://fastdatascience.com/how-does-harmony-work/>. Documentation at <https://harmonydata.ac.uk/harmony-r-released/>.

This package provides a multi-core R package that allows for the statistical modeling of multi-group multivariate mixed data using Gaussian graphical models. Combining the Gaussian copula framework with the fused graphical lasso penalty, the heteromixgm package can handle a wide variety of datasets found in various sciences. The package also includes an option to perform model selection using the AIC, BIC and EBIC information criteria, a function that plots partial correlation graphs based on the selected precision matrices, as well as simulate mixed heterogeneous data for exploratory or simulation purposes and one multi-group multivariate mixed agricultural dataset pertaining to maize yields. The package implements the methodological developments found in Hermes et al. (2024) <doi:10.1080/10618600.2023.2289545>.

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 functions implementing change point detection methods using the maximum pairwise Bayes factor approach. Additionally, the package includes tools for generating simulated datasets for comparing and evaluating change point detection techniques.

This R package has been developed with a focus on air pollution and noise but can applied to other exposures. The initial development has been funded by the European Union project BEST-COST. Disclaimer: It is work in progress and the developers are not liable for any calculation errors or inaccuracies resulting from the use of this package. References (in chronological order): WHO (2003a) "Assessing the environmental burden of disease at national and local levels" <https://www.who.int/publications/i/item/9241546204> (accessed October 2025); WHO (2003b) "Comparative quantification of health risks: Conceptual framework and methodological issues" <doi:10.1186/1478-7954-1-1> (accessed October 2025); Miller & Hurley (2003) "Life table methods for quantitative impact assessments in chronic mortality" <doi:10.1136/jech.57.3.200> (accessed October 2025); Steenland & Armstrong (2006) "An Overview of Methods for Calculating the Burden of Disease Due to Specific Risk Factors" <doi:10.1097/01.ede.0000229155.05644.43> (accessed October 2025); Miller (2010) "Report on estimation of mortality impacts of particulate air pollution in London" <https://cleanair.london/app/uploads/CAL-098-Mayors-health-study-report-June-2010-1.pdf> (accessed October 2025); WHO (2011) "Burden of disease from environmental noise" <https://iris.who.int/items/723ab97c-5c33-4e3b-8df1-744aa5bc1c27> (accessed October 2025); Jerrett et al. (2013) "Spatial Analysis of Air Pollution and Mortality in California" <doi:10.1164/rccm.201303-0609OC> (accessed October 2025); GBD 2019 Risk Factors Collaborators (2020) "Global burden of 87 risk factors in 204 countries and territories, 1990â 2019" <doi:10.1016/S0140-6736(20)30752-2> (accessed October 2025); VanderWeele (2019) "Optimal Approximate Conversions of Odds Ratios and Hazard Ratios to Risk Ratios" <doi: 10.1111/biom.13197> (accessed October 2025); WHO (2020) "Health impact assessment of air pollution: AirQ+ life table manual" <https://iris.who.int/bitstream/handle/10665/337683/WHO-EURO-2020-1559-41310-56212-eng.pdf?sequence=1> (accessed October 2025); ETC HE (2022) "Health risk assessment of air pollution and the impact of the new WHO guidelines" <https://www.eionet.europa.eu/etcs/all-etc-reports> (accessed October 2025); Kim et al. (2022) "DALY Estimation Approaches: Understanding and Using the Incidence-based Approach and the Prevalence-based Approach" <doi:10.3961/jpmph.21.597> (accessed October 2025); Pozzer et al. (2022) "Mortality Attributable to Ambient Air Pollution: A Review of Global Estimates" <doi:10.1029/2022GH000711> (accessed October 2025); Teaching group in EBM (2022) "Evidence-based medicine research helper" <https://ebm-helper.cn/en/Conv/HR_RR.html> (accessed October 2025).

This package provides a simple implementation of doughnut plots - pie charts with a blank center. The package is named after Homer Simpson - arguably the best-known lover of doughnuts.

Univariate agglomerative hierarchical clustering with a comprehensive list of choices of a linkage function in O(n*log n) time. The better algorithmic time complexity is paired with an efficient C++ implementation.

Programmatic interface to the Harmonized World Soil Database HWSD web services (<https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1247>). Allows for easy downloads of HWSD soil data directly to your R workspace or your computer. Routines for both single pixel data downloads and gridded data are provided.

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.

This package provides a wrapper around a CSS library called Hover.css', intended for use in shiny applications.

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>).

This package provides a consistent API for hypothesis testing built on principles from Structure and Interpretation of Computer Programs': data abstraction, closure (combining tests yields tests), and higher-order functions (transforming tests). Implements z-tests, Wald tests, likelihood ratio tests, Fisher's method for combining p-values, and multiple testing corrections. Designed for use by other packages that want to wrap their hypothesis tests in a consistent interface.

Simulate haplotypes through meioses. Allows specification of population parameters.

This package provides tools for estimating sample sizes primarily based on heritability, while also considering additional parameters such as statistical power and fold change. The package normalizes heritability values according to trait-specific heritability and classification to enhance accuracy in sample size estimation.

This package provides a local haplotyping tool for use in trait association and trait prediction analyses pipelines. HaploVar enables users take single nucleotide polymorphisms (SNPs) (in VCF format) and a linkage disequilibrium (LD) matrix, calculate local haplotypes and format the output to be compatible with a wide range of trait association and trait prediction tools. The local haplotypes are calculated from the LD matrix using a clustering algorithm called density-based spatial clustering of applications with noise ('DBSCAN') (Ester et al., 1996) <ISBN: 1577350049>.

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>.

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.

Miscellaneous convenience functions and wrapper functions to convert frequencies between Hz, semitones, mel and Bark, to create a matrix of dummy columns from a factor, to determine whether x lies in range [a,b], and to add a bracketed line to an existing plot. This package also contains an example data set of a stratified sample of 80 talkers of Dutch.

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>.