Distribution free heteroscedastic tests for functional data. The following tests are included in this package: test of no main treatment or contrast effect and no simple treatment effect given in Wang, Higgins, and Blasi (2010) <doi:10.1016/j.spl.2009.11.016>, no main time effect, and no interaction effect based on original observations given in Wang and Akritas (2010a) <doi:10.1080/10485250903171621> and tests based on ranks given in Wang and Akritas (2010b) <doi:10.1016/j.jmva.2010.03.012>.

This package provides a set of R functions which implements Hotelling's T^2 test and some variants of it. Functions are also included for Aitchison's additive log ratio and centred log ratio transformations.

Returns a Hasse diagram of the layout structure (Bate and Chatfield (2016)) <doi:10.1080/00224065.2016.11918173> or the restricted layout structure (Bate and Chatfield (2016)) <doi:10.1080/00224065.2016.11918174> of an experimental design.

This package provides tools to estimate, compare, and visualize healthcare resource utilization using data derived from electronic health records or real-world evidence sources. The package supports pre index and post index analysis, patient cohort comparison, and customizable summaries and visualizations for clinical and health economics research. Methods implemented are based on Scott et al. (2022) <doi:10.1080/13696998.2022.2037917> and Xia et al. (2024) <doi:10.14309/ajg.0000000000002901>.

This package provides a collection of utilities that support creation of network attributes for hydrologic networks. Methods and algorithms implemented are documented in Moore et al. (2019) <doi:10.3133/ofr20191096>), Cormen and Leiserson (2022) <ISBN:9780262046305> and Verdin and Verdin (1999) <doi:10.1016/S0022-1694(99)00011-6>.

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>.

This package creates nomogram visualizations for penalized Cox regression models, with the support of reproducible survival model building, validation, calibration, and comparison for high-dimensional data.

Implementation of S4 class of sets and multisets of numbers. The implementation is based on the hash table from the package hash'. Quick operations are allowed when the set is a dynamic object. The implementation is discussed in detail in Ceoldo and Wit (2023) <arXiv:2304.09809>.

Helper functions for creating reproducible hexagon sticker purely in R.

This package contains data for software hotspot analysis, along with a function performing the analysis itself.

This algorithm is described in detail in the paper "Hedging Forecast Combinations With an Application to the Random Forest" by Beck et al. (2024) <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5032102>. The package provides a function hedgedrf() that can be used to train a Hedged Random Forest model on a dataset, and a function predict.hedgedrf() that can be used to make predictions with the model.

An R API wrapper for the Hystreet project <https://hystreet.com>. Hystreet provides pedestrian counts in different cities in Germany.

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.

Perform forensic handwriting analysis of two scanned handwritten documents. This package implements the statistical method described by Madeline Johnson and Danica Ommen (2021) <doi:10.1002/sam.11566>. Similarity measures and a random forest produce a score-based likelihood ratio that quantifies the strength of the evidence in favor of the documents being written by the same writer or different writers.

The classical Markowitz's mean-variance portfolio formulation ignores heavy tails and skewness. High-order portfolios use higher order moments to better characterize the return distribution. Different formulations and fast algorithms are proposed for high-order portfolios based on the mean, variance, skewness, and kurtosis. The package is based on the papers: R. Zhou and D. P. Palomar (2021). "Solving High-Order Portfolios via Successive Convex Approximation Algorithms." <arXiv:2008.00863>. X. Wang, R. Zhou, J. Ying, and D. P. Palomar (2022). "Efficient and Scalable High-Order Portfolios Design via Parametric Skew-t Distribution." <arXiv:2206.02412>.

Simple tools for converting columns to new data types. Intuitive functions for columns with missing values.

Graphical model is an informative and powerful tool to explore the conditional dependence relationships among variables. The traditional Gaussian graphical model and its extensions either have a Gaussian assumption on the data distribution or assume the data are homogeneous. However, there are data with complex distributions violating these two assumptions. For example, the air pollutant concentration records are non-negative and, hence, non-Gaussian. Moreover, due to climate changes, distributions of these concentration records in different months of a year can be far different, which means it is uncertain whether datasets from different months are homogeneous. Methods with a Gaussian or homogeneous assumption may incorrectly model the conditional dependence relationships among variables. Therefore, we propose a heterogeneous graphical model for non-negative data (HGMND) to simultaneously cluster multiple datasets and estimate the conditional dependence matrix of variables from a non-Gaussian and non-negative exponential family in each cluster.

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.

This package performs multiple hot-deck imputation of categorical and continuous variables in a data frame.

Implementing Hierarchical Bayesian Small Area Estimation models using the brms package as the computational backend. The modeling framework follows the methodological foundations described in area-level models. This package is designed to facilitate a principled Bayesian workflow, enabling users to conduct prior predictive checks, model fitting, posterior predictive checks, model comparison, and sensitivity analysis in a coherent and reproducible manner. It supports flexible model specifications via brms and promotes transparency in model development, aligned with the recommendations of modern Bayesian data analysis practices, implementing methods described in Rao and Molina (2015) <doi:10.1002/9781118735855>.

This package provides tools to generate synthetic electronic health records including patients, encounters, vitals, labs, medications, procedures, and allergies, with optional COVID-19-focused and computed tomography (CT)-research views, and export them to comma separated values ('CSV'), SQLite', and Excel formats for researchers and developers.

Template R package with minimal setup to use Rust code in R without hacks or frameworks. Includes basic examples of importing cargo dependencies, spawning threads and passing numbers or strings from Rust to R. Cargo crates are automatically vendored in the R source package to support offline installation. The GitHub repository for this package has more details and also explains how to set up CI. This project was first presented at Erum2018 to showcase R-Rust integration <https://jeroen.github.io/erum2018/>; for a real world use-case, see the gifski package on CRAN'.

Calculate and visualize Healthy Eating Index (HEI) scores from National Health and Nutrition Examination Survey 24-hour dietary recall data utilizing three methods recommended by the National Cancer Institute (2024) <https://epi.grants.cancer.gov/hei/hei-methods-and-calculations.html#:~:text=To%20use%20the%20simple%20HEI,the%20total%20scores%20across%20individuals.>. Effortlessly analyze HEI scores across different demographic groups and years.

Read, plot, manipulate and process hydro-meteorological data from Argentina and Chile.