Generates HIDECAN plots that summarise and combine the results of genome-wide association studies (GWAS) and transcriptomics differential expression analyses (DE), along with manually curated candidate genes of interest. The HIDECAN plot is presented in Angelin-Bonnet et al. (2023) (currently in review).

Takes the MinT implementation of the hts'<https://cran.r-project.org/package=hts> package and adapts it to allow degenerate hierarchical structures. Instead of the "nodes" argument, this function takes an S matrix which is more versatile in the structures it allows. For a demo, see Steinmeister and Pauly (2024)<doi:10.15488/17729>. The MinT algorithm is based on Wickramasuriya et al. (2019)<doi:10.1080/01621459.2018.1448825>.

Identification of recombination events, haplotype reconstruction, sire imputation and pedigree reconstruction using half-sib family SNP data.

In streaming data analysis, it is crucial to detect significant shifts in the data distribution or the accuracy of predictive models over time, a phenomenon known as concept drift. The package aims to identify when concept drift occurs and provide methodologies for adapting models in non-stationary environments. It offers a range of state-of-the-art techniques for detecting concept drift and maintaining model performance. Additionally, the package provides tools for adapting models in response to these changes, ensuring continuous and accurate predictions in dynamic contexts. Methods for concept drift detection are described in Tavares (2022) <doi:10.1007/s12530-021-09415-z>.

Input multiple versions of a source document, and receive HTML code for a highlighted version of the source document indicating the frequency of occurrence of phrases in the different versions. This method is described in Chapter 3 of Rogers (2024) <https://digitalcommons.unl.edu/dissertations/AAI31240449/>.

Builds on the EMD package to provide additional tools for empirical mode decomposition (EMD) and Hilbert spectral analysis. It also implements the ensemble empirical decomposition (EEMD) and the complete ensemble empirical mode decomposition (CEEMD) methods to avoid mode mixing and intermittency problems found in EMD analysis. The package comes with several plotting methods that can be used to view intrinsic mode functions, the HHT spectrum, and the Fourier spectrum.

Implemented here are procedures for fitting hierarchical generalized linear models (HGLM). It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the mean model. As statistical models, HGLMs were initially developed by Lee and Nelder (1996) <https://www.jstor.org/stable/2346105?seq=1>. We provide an implementation (Ronnegard, Alam and Shen 2010) <https://journal.r-project.org/archive/2010-2/RJournal_2010-2_Roennegaard~et~al.pdf> following Lee, Nelder and Pawitan (2006) <ISBN: 9781420011340> with algorithms extended for spatial modeling (Alam, Ronnegard and Shen 2015) <https://journal.r-project.org/archive/2015/RJ-2015-017/RJ-2015-017.pdf>.

This package provides a two-step double-robust method to estimate the conditional average treatment effects (CATE) with potentially high-dimensional covariate(s). In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. The second stage consists of a low-dimensional local linear regression, reducing CATE to a function of the covariate(s) of interest. The CATE estimator implemented in this package not only allows for high-dimensional data, but also has the â double robustnessâ property: either the model for the propensity score or the models for the conditional means of the potential outcomes are allowed to be misspecified (but not both). This package is based on the paper by Fan et al., "Estimation of Conditional Average Treatment Effects With High-Dimensional Data" (2022), Journal of Business & Economic Statistics <doi:10.1080/07350015.2020.1811102>.

The Gene Ontology (GO) Consortium <https://geneontology.org/> organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as GoMiner (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Microarray studies are usually analyzed with BP, whereas proteomics researchers often prefer CC. To capture the benefit of both of those ontologies, I now present an enhancement of the existing two-dimensional version of High-Throughput GoMiner ('HTGM2D'), which is called HTGM4D'. The original HTGM2D is augmented by adding two instances of the original GoMiner genes versus categories heatmaps, aligned with the categories axes of the HTGM2D heatmap.

Efficient tools for parsing and standardizing Australian addresses from textual data. It utilizes optimized algorithms to accurately identify and extract components of addresses, such as street names, types, and postcodes, especially for large batched data in contexts where sending addresses to internet services may be slow or inappropriate. The core functionality is built on fast string processing techniques to handle variations in address formats and abbreviations commonly found in Australian address data. Designed for data scientists, urban planners, and logistics analysts, the package facilitates the cleaning and normalization of address information, supporting better data integration and analysis in urban studies, geography, and related fields.

This package provides diagnostic functions for integrating longitudinal soil, plant and microbial observations during redox disturbance and recovery. Functions calculate stoichiometric potential oxygen demand, accessible electron capacity from explicitly supplied inventories and kinetic parameters, recovery signatures, fixed-reference domain scores, and exploratory multiblock scores with observation-coverage diagnostics. Memory is represented as a holobiont state accumulating from mineralogical, plant-acclimation and microbial-community legacies. An illustrative simulator produces closed Fe and Mn inventories alongside synthetic observations; its carbon, nitrogen, sulfur and oxygen budgets are not closed and its parameters are not calibrated to field rates. Simulation benchmarks assess agreement with a prescribed synthetic target and do not constitute empirical validation or parameter identification. Accuracy assessment is cluster-aware: intervals come from resampling whole trajectories, agreement is reported as Lin's concordance coefficient alongside correlation, and mean squared error is partitioned into bias, variance mismatch and lack of correlation. The measured quantities follow Sander, Hofstetter and Gorski (2015) <doi:10.1021/acs.est.5b00006> for mediated electrochemical determination of electron-accepting and electron-donating capacity, Kluepfel, Piepenbrock, Kappler and Sander (2014) <doi:10.1038/ngeo2084> for regeneration of electron-accepting capacity across repeated anoxic periods, Thompson, Chadwick, Rancourt and Chorover (2006) <doi:10.1016/j.gca.2005.12.005> for the increase in iron-oxide crystallinity under redox oscillation, and Keiluweit, Wanzek, Kleber, Nico and Fendorf (2017) <doi:10.1038/s41467-017-01406-6> for anaerobic microsites in otherwise aerobic soil. Agreement statistics follow Lin (1989) <doi:10.2307/2532051> and Kobayashi and Salam (2000) <doi:10.2134/agronj2000.922345x>.

Calculate clinical scores for hidradenitis suppurativa (HS), a dermatologic disease. The scores are typically used for evaluation of efficacy in clinical trials. The scores are not commonly used in clinical practice. The specific scores implemented are Hidradenitis Suppurativa Clinical Response (HiSCR) (Kimball, et al. (2015) <doi:10.1111/jdv.13216>), Hidradenitis Suppurativa Area and Severity Index Revised (HASI-R) (Goldfarb, et al. (2020) <doi:10.1111/bjd.19565>), hidradenitis suppurativa Physician Global Assessment (HS PGA) (Marzano, et al. (2020) <doi:10.1111/jdv.16328>), and the International Hidradenitis Suppurativa Severity Score System (IHS4) (Zouboulis, et al. (2017) <doi:10.1111/bjd.15748>).

Allows to evaluate Higher Order Assortativity of complex networks defined through objects of class igraph from the package of the same name. The package returns a result also for directed and weighted graphs. References, Arcagni, A., Grassi, R., Stefani, S., & Torriero, A. (2017) <doi:10.1016/j.ejor.2017.04.028> Arcagni, A., Grassi, R., Stefani, S., & Torriero, A. (2021) <doi:10.1016/j.jbusres.2019.10.008> Arcagni, A., Cerqueti, R., & Grassi, R. (2023) <doi:10.48550/arXiv.2304.01737>.

Allows to estimate and test high-dimensional mediation effects based on advanced mediator screening and penalized regression techniques. Methods used in the package refer to Zhang H, Zheng Y, Hou L, Liu L, HIMA: An R Package for High-Dimensional Mediation Analysis. Journal of Data Science. (2025). <doi:10.6339/25-JDS1192>.

Sets up and executes a HiSSE model (Hidden State Speciation and Extinction) on a phylogeny and character sets to test for hidden shifts in trait dependent rates of diversification. Beaulieu and O'Meara (2016) <doi:10.1093/sysbio/syw022>.

Higher order likelihood inference is a promising approach for analyzing small sample size data. The holi package provides web applications for higher order likelihood inference. It currently supports linear, logistic, and Poisson generalized linear models through the rstar_glm() function, based on Pierce and Bellio (2017) <doi:10.1111/insr.12232> and likelihoodAsy'. The package offers two main features: LA_rstar(), which launches an interactive shiny application allowing users to fit models with rstar_glm() through their web browser, and sim_rstar_glm_pgsql(), which streamlines the process of launching a web-based shiny simulation application that saves results to a user-created PostgreSQL database.

By binding R functions and the Highcharts <http://www.highcharts.com/> charting library, hpackedbubble package provides a simple way to draw split packed bubble charts.

Pfafstetter Hydrological Codes as cited in Verdin and Verdin (1999) <doi: 10.1016/S0022-1694(99)00011-6> are decoded for upstream or downstream queries.

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/>.

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.

This package implements an efficient algorithm for fitting the entire regularization path of quantile regression models with elastic-net penalties using a generalized coordinate descent scheme. The framework also supports SCAD and MCP penalties. It is designed for high-dimensional datasets and emphasizes numerical accuracy and computational efficiency. This package implements the algorithms proposed in Tang, Q., Zhang, Y., & Wang, B. (2022) <https://openreview.net/pdf?id=RvwMTDYTOb>.

Kernel density estimation with hexagonal grid for bivariate data. Hexagonal grid has many beneficial properties like equidistant neighbours and less edge bias, making it better for spatial analyses than the more commonly used rectangular grid. Carr, D. B. et al. (1987) <doi:10.2307/2289444>. Diggle, P. J. (2010) <doi:10.1201/9781420072884>. Hill, B. (2017) <https://blog.bruce-hill.com/meandering-triangles>. Jones, M. C. (1993) <doi:10.1007/BF00147776>.

Estimation procedures and goodness-of-fit test for several Markov regime switching models and mixtures of bivariate copula models. The goodness-of-fit test is based on a Cramer-von Mises statistic and uses Rosenblatt's transform and parametric bootstrap to estimate the p-value. The proposed methodologies are described in Nasri, Remillard and Thioub (2020) <doi:10.1002/cjs.11534>.

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>.