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).

This package creates a responsive HTML file with tiled hexagonal logos for packages in an R session. Tiles can be also be generated for a custom set of packages specified with a character vector. Output can be saved as a static screenshot in PNG format using a headless browser.

Estimation of high-dimensional multi-response regression with heterogeneous noises under Heterogeneous group square-root Lasso penalty. For details see: Ren, Z., Kang, Y., Fan, Y. and Lv, J. (2018)<arXiv:1606.03803>.

This package provides functions for the management and treatment of hydrology and meteorology time-series stored in a Sqlite data base.

Fitting hidden Markov models using automatic differentiation and Laplace approximation, allowing for fast inference and flexible covariate effects (including random effects and smoothing splines) on model parameters. The package is described by Michelot (2025) <doi:10.18637/jss.v114.i05>.

This package provides functions to perform dimensionality reduction for classification if the covariance matrices of the classes are unequal.

Computes the expectation of the number of transmissions and receptions considering a Hop-by-Hop transport model with limited number of retransmissions per packet. It provides the theoretical results shown in Palma et. al.(2016) <DOI:10.1109/TLA.2016.7555237> and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.

This package provides a dependency free interface to the H3 geospatial indexing system utilizing the Rust library h3o <https://github.com/HydroniumLabs/h3o> via the extendr library <https://github.com/extendr/extendr>.

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

Using the MDL principle, it is possible to estimate parameters for a histogram-like model. The package contains the implementation of such an estimation method.

An implementation of Random Forest-based two-sample tests as introduced in Hediger & Michel & Naef (2022).

Fits sparse interaction models for continuous and binary responses subject to the strong (or weak) hierarchy restriction that an interaction between two variables only be included if both (or at least one of) the variables is included as a main effect. For more details, see Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141.

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>).

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.

This package provides a visualization suite primarily designed for single-cell RNA-sequencing data analysis applications but well-suited for other purposes as well. It introduces novel plots to represent two-variable and frequency data and optimizes some commonly used plotting options (e.g., correlation, network, density, alluvial and volcano plots) for ease of usage and flexibility.

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. To capture the benefit of all three ontologies, I developed HTGM3D', a three-dimensional version of GoMiner'.

This package provides a set of tools supporting more flexible heatmaps. The graphics is grid-like using the old graphics system. The main function is heatmap.n2(), which is a wrapper around the various functions constructing individual parts of the heatmap, like sidebars, picket plots, legends etc. The function supports zooming and splitting, i.e., having (unlimited) small heatmaps underneath each other in one plot deriving from the same data set, e.g., clustered and ordered by a supervised clustering method.

This package contains the National Health and Nutrition Examination Survey 24-hour dietary recall data and Healthy Eating Index scoring standards used by the heiscore package.

This package provides inverse-free high-dimensional location tests for two-sample and general linear hypothesis testing (GLHT) problems under equal or unequal covariance structures. The package implements classical normal-approximation procedures, scale-invariant procedures, normal-reference procedures based on covariance-matched Gaussian companions, and F-type normal-reference calibrations for heteroscedastic Behrens-Fisher and GLHT settings. Implemented two-sample normal-approximation and scale-invariant procedures include Bai and Saranadasa (1996) <https://www.jstor.org/stable/24306018>, Chen and Qin (2010) <doi:10.1214/09-aos716>, Srivastava and Du (2008) <doi:10.1016/j.jmva.2006.11.002>, and Srivastava et al. (2013) <doi:10.1016/j.jmva.2012.08.014>. Implemented two-sample normal-reference procedures include Zhang, Guo, Zhou and Cheng (2020) <doi:10.1080/01621459.2019.1604366>, Zhang, Zhou, Guo and Zhu (2021) <doi:10.1016/j.jspi.2020.11.008>, Zhang, Zhu and Zhang (2020) <doi:10.1016/j.ecosta.2019.12.002>, Zhang, Zhu and Zhang (2023) <doi:10.1080/02664763.2020.1834516>, Zhang and Zhu (2022) <doi:10.1080/10485252.2021.2015768>, Zhang and Zhu (2022) <doi:10.1007/s42519-021-00232-w>, and Zhu, Wang and Zhang (2023) <doi:10.1007/s00180-023-01433-6>. Implemented GLHT normal-approximation procedures include Fujikoshi et al. (2004) <doi:10.14490/jjss.34.19>, Srivastava and Fujikoshi (2006) <doi:10.1016/j.jmva.2005.08.010>, Yamada and Srivastava (2012) <doi:10.1080/03610926.2011.581786>, Schott (2007) <doi:10.1016/j.jmva.2006.11.007>, and Zhou, Guo and Zhang (2017) <doi:10.1016/j.jspi.2017.03.005>. Implemented GLHT normal-reference procedures include Zhang, Guo and Zhou (2017) <doi:10.1016/j.jmva.2017.01.002>, Zhang, Zhou and Guo (2022) <doi:10.1016/j.jmva.2021.104816>, Zhu, Zhang and Zhang (2022) <doi:10.5705/ss.202020.0362>, Zhu and Zhang (2022) <doi:10.1007/s00180-021-01110-6>, Zhang and Zhu (2022) <doi:10.1016/j.csda.2021.107385>, and Cao et al. (2024) <doi:10.1007/s00362-024-01530-8>. The package also includes the random-integration normal-approximation GLHT procedure of Li et al. (2025) <doi:10.1007/s00362-024-01624-3>. A package-level overview is given in Wang, Zhu and Zhang (2026) <doi:10.1016/j.csda.2025.108269>.

Automatic construction of regular and irregular histograms as described in Rozenholc/Mildenberger/Gather (2010).

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>.

Identifying labeled compounds in a 13C-tracer experiment in non-targeted fashion is a cumbersome process. This package facilitates such type of analyses by providing high level quality control plots, deconvoluting and evaluating spectra and performing a multitude of tests in an automatic fashion. The main idea is to use changing intensity ratios of ion pairs from peak list generated with xcms as candidates and evaluate those against base peak chromatograms and spectra information within the raw measurement data automatically. The functionality is described in Hoffmann et al. (2018) <doi:10.1021/acs.analchem.8b00356>.

Tests for a treatment effect using surrogate marker information accounting for heterogeneity in the utility of the surrogate. Details are described in Parast et al (2022) <arXiv:2209.08315>.