This package provides helper functions for analysing patient data in hyperthermic intraperitoneal chemotherapy (HIPEC) workflows. Includes functions to estimate peritoneal surface area (PSA), summarise registry data, and produce reporting graphics. Body surface area calculations are based on Du Bois and Du Bois (1916) <doi:10.1001/archinte.1916.00080130010002>.

This package implements methods developed by Ding, Feller, and Miratrix (2016) <doi:10.1111/rssb.12124> <doi:10.48550/arXiv.1412.5000>, and Ding, Feller, and Miratrix (2018) <doi:10.1080/01621459.2017.1407322> <doi:10.48550/arXiv.1605.06566> for testing whether there is unexplained variation in treatment effects across observations, and for characterizing the extent of the explained and unexplained variation in treatment effects. The package includes wrapper functions implementing the proposed methods, as well as helper functions for analyzing and visualizing the results of the test.

Read, plot, manipulate and process hydro-meteorological data records (with special features for Argentina and Chile data-sets).

Solves Hidden Markov Models (HMMs) via matrix and tensor decomposition. Converts observation sequences to co-occurrence matrices/tensors and applies Symmetric Non-negative Matrix Factorization (symNMF), Singular Value Decomposition (SVD), CANDECOMP/PARAFAC (CP) decomposition, or Tensor-Train (TT) decomposition to recover HMM parameters. Also provides standard HMM algorithms (Forward, Backward, Viterbi, Baum-Welch) for comparison. The spectral learning approach for HMMs is based on Hsu, Kakade, and Zhang (2012) <doi:10.1016/j.jcss.2011.12.025>. The symNMF method is described in Kuang, Yun, and Park (2015) <doi:10.1007/s10898-014-0247-2>. The Tensor-Train decomposition is described in Oseledets (2011) <doi:10.1137/090752286>.

This package provides functions to access data from the US Department of Housing and Urban Development <https://www.huduser.gov/portal/dataset/fmr-api.html>.

Converts among many citation formats, including BibTeX', Citeproc', Codemeta', RDF XML', RIS', Schema.org', and Citation File Format'. A low level R6 class is provided, as well as stand-alone functions for each citation format for both read and write.

This package provides a unified, extensible interface for discovering hydrological stations and downloading hydrological daily time series (e.g., water discharge, water level, water temperature) and discrete water-quality observations from national and regional public APIs. Water-quality observations are retained at their original sampling timestamps. Includes a provider registry, S3 generics stations and timeseries', licensing metadata, date-range and complete history modes, rate limiting and retries, optional authentication via environment variables, tidy outputs, UTF-8 to ASCII transliteration, and WGS84 coordinates. Designed for reproducible workflows and straightforward addition of new providers. Background and use cases are described in Farber et al. (2025) <doi:10.5194/essd-17-4613-2025> and Farber et al. (2023) <doi:10.57757/IUGG23-2838>.

Package that simplifies the use of the HPZone API. Most of the annoying and labor-intensive parts of the interface are handled by wrapper functions. Note that the API and its details are not publicly available. Information can be found at <https://www.ggdghorkennisnet.nl/groep/726-platform-infectieziekte-epidemiologen/documenten/map/9609> for those with access.

Create dynamic, data-driven text. Given two values, a list of talking points is generated and can be combined using string interpolation. Based on the glue package.

This package provides a set of functions to estimate haziness of an image based on RGB bands. It returns a haze factor, varying from 0 to 1, a metric for fogginess and cloudiness. The package also presents additional functions to estimate brightness, darkness and contrast rasters of the RGB image. This package can be used for several applications such as inference of weather quality data and performing environmental studies from interpreting digital images.

By analyzing time series, it is possible to observe significant changes in the behavior of observations that frequently characterize events. Events present themselves as anomalies, change points, or motifs. In the literature, there are several methods for detecting events. However, searching for a suitable time series method is a complex task, especially considering that the nature of events is often unknown. This work presents Harbinger, a framework for integrating and analyzing event detection methods. Harbinger contains several state-of-the-art methods described in Salles et al. (2020) <doi:10.5753/sbbd.2020.13626>.

Bipartite graph-based hierarchical clustering, developed for pharmacogenomic datasets and datasets sharing the same data structure. The goal is to construct a hierarchical clustering of groups of samples based on association patterns between two sets of variables. In the context of pharmacogenomic datasets, the samples are cell lines, and the two sets of variables are typically expression levels and drug sensitivity values. For this method, sparse canonical correlation analysis from Lee, W., Lee, D., Lee, Y. and Pawitan, Y. (2011) <doi:10.2202/1544-6115.1638> is first applied to extract association patterns for each group of samples. Then, a nuclear norm-based dissimilarity measure is used to construct a dissimilarity matrix between groups based on the extracted associations. Finally, hierarchical clustering is applied.

Processing, analysis and visualization of Hydrogen Deuterium eXchange monitored by Mass Spectrometry experiments (HDX-MS). HaDeX2 introduces a new standardized and reproducible workflow for the analysis of the HDX-MS data, including uncertainty propagation, data aggregation and visualization on 3D structure. Additionally, it covers data exploration, quality control and generation of publication-quality figures. All functionalities are also available in the accompanying shiny app.

Code Syntax Highlighting made easy for code snippets or complete files. Whether you're documenting your data analysis or creating interactive shiny apps.

Generates a fit plot for diagnosing misspecification in models of binary dependent variables, and calculates the related heatmap fit statistic described in Esarey and Pierce (2012) <DOI:10.1093/pan/mps026>.

State-of-the-art Multi-Objective Particle Swarm Optimiser (MOPSO), based on the algorithm developed by Lin et al. (2018) <doi:10.1109/TEVC.2016.2631279> with improvements described by Marinao-Rivas & Zambrano-Bigiarini (2020) <doi:10.1109/LA-CCI48322.2021.9769844>. This package is inspired by and closely follows the philosophy of the single objective hydroPSO R package ((Zambrano-Bigiarini & Rojas, 2013) <doi:10.1016/j.envsoft.2013.01.004>), and can be used for global optimisation of non-smooth and non-linear R functions and R-base models (e.g., TUWmodel', GR4J', GR6J'). However, the main focus of hydroMOPSO is optimising environmental and other real-world models that need to be run from the system console (e.g., SWAT+'). hydroMOPSO communicates with the model to be optimised through its input and output files, without requiring modifying its source code. Thanks to its flexible design and the availability of several fine-tuning options, hydroMOPSO can tackle a wide range of multi-objective optimisation problems (e.g., multi-objective functions, multiple model variables, multiple periods). Finally, hydroMOPSO is designed to run on multi-core machines or network clusters, to alleviate the computational burden of complex models with long execution time.

The HMS (Hierarchic Memetic Strategy) is a composite global optimization strategy consisting of a multi-population evolutionary strategy and some auxiliary methods. The HMS makes use of a dynamically-evolving data structure that provides an organization among the component populations. It is a tree with a fixed maximal height and variable internal node degree. Each component population is governed by a particular evolutionary engine. This package provides a simple R implementation with examples of using different genetic algorithms as the population engines. References: J. Sawicki, M. Å oÅ , M. SmoÅ ka, J. Alvarez-Aramberri (2022) <doi:10.1007/s11047-020-09836-w>.

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.

Fast, documented implementations of robust estimation, testing, dimension reduction, classification, and clustering methods for high-dimensional elliptically symmetric data. Computational kernels use Rcpp and RcppArmadillo'. The package follows methods reviewed in Feng (2026), "High-Dimensional Data Analysis for Elliptically Symmetric Distributions" <https://github.com/flnankai/HDElliptical/releases>.

Decode elements of the Australian Higher Education Information Management System (HEIMS) data for clarity and performance. HEIMS is the record system of the Department of Education, Australia to record enrolments and completions in Australia's higher education system, as well as a range of relevant information. For more information, including the source of the data dictionary, see <https://web.archive.org/web/20180210074903/http://heimshelp.education.gov.au/sites/heimshelp/dictionary/pages/data-element-dictionary>. That collection has since been superseded by the Tertiary Collection of Student Information, whose data element dictionary is at <https://www.tcsisupport.gov.au/element>.

An interactive Shiny dashboard for visualizing and exploring key metrics related to HIV/AIDS, including prevalence, incidence, mortality, and treatment coverage. The dashboard is designed to work with a dataset containing specific columns with standardized names. These columns must be present in the input data for the app to function properly: year: Numeric year of the data (e.g. 2010, 2021); sex: Gender classification (e.g. Male, Female); age_group: Age bracket (e.g. 15â 24, 25â 34); hiv_prevalence: Estimated HIV prevalence percentage; hiv_incidence: Number of new HIV cases per year; aids_deaths: Total AIDS-related deaths; plhiv: Estimated number of people living with HIV; art_coverage: Percentage receiving antiretroviral therapy (ART); testing_coverage: HIV testing services coverage; causes: Description of likely HIV transmission cause (e.g. unprotected sex, drug use). The dataset structure must strictly follow this column naming convention for the dashboard to render correctly.

Initializes a class that obtains API credentials and provides a method to use those credentials to make GET requests to the Hakai API server. Usage instructions are documented at <https://hakaiinstitute.github.io/hakai-api/>.

This package provides statistical and graphical tools for the analysis of hydroponic crop production data. The package includes functions for descriptive statistical analysis, data visualization, correlation analysis, heatmap generation, and graphical summaries of plant growth and nutrient-related variables. These tools support researchers, students, and practitioners in evaluating crop performance and environmental conditions in hydroponic cultivation systems. The package utilizes standard statistical methods implemented in R for data exploration and visualization. Methods are described in James et al. (2021, ISBN:9781071614172) and Wickham (2016, ISBN:9783319242750).

Fitting hidden Markov models of learning under the cognitive diagnosis framework. The estimation of the hidden Markov diagnostic classification model, the first order hidden Markov model, the reduced-reparameterized unified learning model, and the joint learning model for responses and response times.