This package provides functions for survey data including svydesign objects from the survey package that call ggplot2 to make bar charts, histograms, boxplots, and hexplots of survey data.

Kernel regularized least squares, also known as kernel ridge regression, is a flexible machine learning method. This package implements this method by providing a smooth term for use with mgcv and uses random sketching to facilitate scalable estimation on large datasets. It provides additional functions for calculating marginal effects after estimation and for use with ensembles ('SuperLearning'), double/debiased machine learning ('DoubleML'), and robust/clustered standard errors ('sandwich'). Chang and Goplerud (2024) <doi:10.1017/pan.2023.27> provide further details.

This package provides shortcuts in extracting useful data points and summarizing waveform data. It is optimized for speed to work efficiently with large data sets so you can get to the analysis phase more quickly. It also utilizes a user-friendly format for use by both beginners and seasoned R users.

Shiny application for the analysis of groundwater monitoring data, designed to work with simple time-series data for solute concentration and ground water elevation, but can also plot non-aqueous phase liquid (NAPL) thickness if required. Also provides the import of a site basemap in GIS shapefile format.

This package provides a collection of GIS (Geographic Information System) functions in R, created for use in Statistics Norway. The functions are primarily related to network analysis on the Norwegian road network.

An S3 class groupedHyperframe that inherits from hyper data frame. Batch processes on point-pattern hyper column. Aggregation of function-value-table hyper column(s) and numeric hyper column(s) over a nested grouping structure.

Geographical detectors for measuring spatial stratified heterogeneity, as described in Jinfeng Wang (2010) <doi:10.1080/13658810802443457> and Jinfeng Wang (2016) <doi:10.1016/j.ecolind.2016.02.052>. Includes the optimal discretization of continuous data, four primary functions of geographical detectors, comparison of size effects of spatial unit and the visualizations of results. To use the package and to refer the descriptions of the package, methods and case datasets, please cite Yongze Song (2020) <doi:10.1080/15481603.2020.1760434>. The model has been applied in factor exploration of road performance and multi-scale spatial segmentation for network data, as described in Yongze Song (2018) <doi:10.3390/rs10111696> and Yongze Song (2020) <doi:10.1109/TITS.2020.3001193>, respectively.

Group Bayesian Networks: This package implements the inference of group Bayesian networks based on hierarchical feature clustering, and the adaptive refinement of the grouping regarding an outcome of interest, as described in Becker et. al (2021) <doi: 10.1371/journal.pcbi.1008735>.

Genotyping of triploid individuals from luminescence data (marker probeset A and B). Works also for diploids. Two main functions: Run_Clustering() that regroups individuals with a same genotype based on proximity and Run_Genotyping() that assigns a genotype to each cluster. For Shiny interface use: launch_GenoShiny().

This package creates presentation-ready tables summarizing data sets, regression models, and more. The code to create the tables is concise and highly customizable. Data frames can be summarized with any function, e.g. mean(), median(), even user-written functions. Regression models are summarized and include the reference rows for categorical variables. Common regression models, such as logistic regression and Cox proportional hazards regression, are automatically identified and the tables are pre-filled with appropriate column headers.

Designed to customize ggplot graphics according to the institutional identity of the University of Ljubljana.

The correlations and linkage disequilibrium between tests can vary as a function of minor allele frequency thresholds used to filter variants, and also varies with different choices of test statistic for region-based tests. Appropriate genome-wide significance thresholds can be estimated empirically through permutation on only a small proportion of the whole genome.

An implementation of functions to display Greek letters on the RStudio (include subscript and superscript indexes) and RGui (without subscripts and only with superscript 1, 2 or 3; because RGui doesn't support printing the corresponding Unicode characters as a string: all subscripts ranging from 0 to 9 and superscripts equal to 0, 4, 5, 6, 7, 8 or 9). The functions in this package do not work properly on the R console. Characters are used via Unicode and encoded as UTF-8 to ensure that they can be viewed on all operating systems. Other characters related to mathematics are included, such as the infinity symbol. All this accessible from very simple commands. This is a package that can be used for teaching purposes, the statistical notation for hypothesis testing can be written from this package and so it is possible to build a course from the swirlify package. Another utility of this package is to create new summary functions that contain the functional form of the model adjusted with the Greek letters, thus making the transition from statistical theory to practice easier. In addition, it is a natural extension of the clisymbols package.

The risk plot may be one of the most commonly used figures in tumor genetic data analysis. We can conclude the following two points: Comparing the prediction results of the model with the real survival situation to see whether the survival rate of the high-risk group is lower than that of the low-level group, and whether the survival time of the high-risk group is shorter than that of the low-risk group. The other is to compare the heat map and scatter plot to see the correlation between the predictors and the outcome.

Generalization of supervised principal component regression (SPCR; Bair et al., 2006, <doi:10.1198/016214505000000628>) to support continuous, binary, and discrete variables as outcomes and predictors (inspired by the superpc R package <https://cran.r-project.org/package=superpc>).

This package provides tools to compute the Generalized Measure of Correlation (GMC), a dependence measure accounting for nonlinearity and asymmetry in the relationship between variables. Based on the method proposed by Zheng, Shi, and Zhang (2012) <doi:10.1080/01621459.2012.710509>.

This package provides tools to download comprehensive datasets of local, state, and federal election results in Germany from 1990 to 2025. The package facilitates access to data on turnout, vote shares for major parties, and demographic information across different levels of government (municipal, state, and federal). It offers access to geographically harmonized datasets that account for changes in municipal boundaries over time and incorporate mail-in voting districts. Users can easily retrieve, clean, and standardize German electoral data, making it ready for analysis. Data is sourced from <https://github.com/awiedem/german_election_data>.

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.

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 contains ten datasets used in the chapters and exercises of Paul, Alice (2023) "Health Data Science in R" <https://alicepaul.github.io/health-data-science-using-r/>.

We provide the monthly number of HIV and antiretroviral therapy (ART) cases of male, female, children and transgender as well as for the whole of Pakistan reported at various treatment centers in Pakistan from January 2016 to December 2021. Related works include: a) Imran, M., Nasir, J. A., & Riaz, S. (2018). Regional pattern of HIV cases in Pakistan. Journal of Postgraduate Medical Institute, 32(1), 9-13. <https://jpmi.org.pk/index.php/jpmi/article/view/2108>.

This package provides functions to implement a hierarchical approach which is designed to perform joint analysis of summary statistics using the framework of Mendelian Randomization or transcriptome analysis. Reference: Lai Jiang, Shujing Xu, Nicholas Mancuso, Paul J. Newcombe, David V. Conti (2020). "A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis." <bioRxiv><doi:10.1101/2020.02.03.924241>.

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.

This package creates styled tables for data presentation. Export to HTML, LaTeX, RTF, Word', Excel', PowerPoint', typst', SVG and PNG. Simple, modern interface to manipulate borders, size, position, captions, colours, text styles and number formatting. Table cells can span multiple rows and/or columns. Includes a huxreg function to create regression tables, and quick_* one-liners to print tables to a new document.