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

Machine learning hierarchical risk clustering portfolio allocation strategies. The implemented methods are: Hierarchical risk parity (De Prado, 2016) <DOI: 10.3905/jpm.2016.42.4.059>. Hierarchical clustering-based asset allocation (Raffinot, 2017) <DOI: 10.3905/jpm.2018.44.2.089>. Hierarchical equal risk contribution portfolio (Raffinot, 2018) <DOI: 10.2139/ssrn.3237540>. A Constrained Hierarchical Risk Parity Algorithm with Cluster-based Capital Allocation (Pfitzingera and Katzke, 2019) <https://www.ekon.sun.ac.za/wpapers/2019/wp142019/wp142019.pdf>.

This package provides a simple implementation of doughnut plots - pie charts with a blank center. The package is named after Homer Simpson - arguably the best-known lover of doughnuts.

Two papers published in the early 2000's (Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) and (Zeeberg, B.R., Qin, H., Narashimhan, S., et al. (2005) <doi:10.1186/1471-2105-6-168>) implement GoMiner and High Throughput GoMiner ('HTGM') to map lists of genes to the Gene Ontology (GO) <https://geneontology.org>. Until recently, these were hosted on a server at The National Cancer Institute (NCI). In order to continue providing these services to the bio-medical community, I have developed stand-alone versions. The current package HTGM builds upon my recent package GoMiner'. The output of GoMiner is a heatmap showing the relationship of a single list of genes and the significant categories into which they map. High Throughput GoMiner ('HTGM') integrates the results of the individual GoMiner analyses. The output of HTGM is a heatmap showing the relationship of the significant categories derived from each gene list. The heatmap has only 2 axes, so the identity of the genes are unfortunately "integrated out of the equation." Because the graphic for the heatmap is implemented in Scalable Vector Graphics (SVG) technology, it is relatively easy to hyperlink each picture element to the relevant list of genes. By clicking on the desired picture element, the user can recover the "lost" genes.

An important environmental impact on running water ecosystems is caused by hydropeaking - the discontinuous release of turbine water because of peaks of energy demand. An event-based algorithm is implemented to detect flow fluctuations referring to increase events (IC) and decrease events (DC). For each event, a set of parameters related to the fluctuation intensity is calculated. The framework is introduced in Greimel et al. (2016) "A method to detect and characterize sub-daily flow fluctuations" <doi:10.1002/hyp.10773> and can be used to identify different fluctuation types according to the potential source: e.g., sub-daily flow fluctuations caused by hydropeaking, rainfall, or snow and glacier melt. This is a companion to the package hydroroute', which is used to detect and follow hydropower plant-specific hydropeaking waves at the sub-catchment scale and to describe how hydropeaking flow parameters change along the longitudinal flow path as proposed and validated in Greimel et al. (2022).

Helping to calculate cricket specific problems in a tidy & simple manner.

This package provides functions for processing, analysis and visualization of Hydrogen Deuterium eXchange monitored by Mass Spectrometry experiments (HDX-MS) (<doi:10.1093/bioinformatics/btaa587>). HaDeX introduces a new standardized and reproducible workflow for the analysis of the HDX-MS data, including novel uncertainty intervals. Additionally, it covers data exploration, quality control and generation of publication-quality figures. All functionalities are also available in the in-built Shiny app.

This package provides functions for testing affine hypotheses on the regression coefficient vector in regression models with heteroskedastic errors: (i) a function for computing various test statistics (in particular using HC0-HC4 covariance estimators based on unrestricted or restricted residuals); (ii) a function for numerically approximating the size of a test based on such test statistics and a user-supplied critical value; and, most importantly, (iii) a function for determining size-controlling critical values for such test statistics and a user-supplied significance level (also incorporating a check of conditions under which such a size-controlling critical value exists). The three functions are based on results in Poetscher and Preinerstorfer (2021) "Valid Heteroskedasticity Robust Testing" <doi:10.48550/arXiv.2104.12597>, which will appear as <doi:10.1017/S0266466623000269>.

This package provides tools for processing and analyzing .har and .sl4 files, making it easier for GEMPACK users and GTAP researchers to handle large economic datasets. It simplifies the management of multiple experiment results, enabling faster and more efficient comparisons without complexity. Users can extract, restructure, and merge data seamlessly, ensuring compatibility across different tools. The processed data can be exported and used in R', Stata', Python', Julia', or any software that supports Text, CSV, or Excel formats.

Create publication-quality, 2-dimensional visualizations of alpha-helical peptide sequences. Specifically, allows the user to programmatically generate helical wheels and wenxiang diagrams to provide a bird's eye, top-down view of alpha-helical oligopeptides. See Wadhwa RR, et al. (2018) <doi:10.21105/joss.01008> for more information.

An RStudio Addin for Hippie Expand (AKA Hippie Code Completion or Cyclic Expand Word). This type of completion searches for matching tokens within the user's current source editor file, regardless of file type. By searching only within the current source file, hippie offers a fast way to identify and insert completions that appear around the user's cursor.

This package provides the posterior estimates of the regression coefficients when horseshoe prior is specified. The regression models considered here are logistic model for binary response and log normal accelerated failure time model for right censored survival response. The linear model analysis is also available for completeness. All models provide deviance information criterion and widely applicable information criterion. See <doi:10.1111/rssc.12377> Maity et. al. (2019) <doi:10.1111/biom.13132> Maity et. al. (2020).

The harmonic mean p-value (HMP) test combines p-values and corrects for multiple testing while controlling the strong-sense family-wise error rate. It is more powerful than common alternatives including Bonferroni and Simes procedures when combining large proportions of all the p-values, at the cost of slightly lower power when combining small proportions of all the p-values. It is more stringent than controlling the false discovery rate, and possesses theoretical robustness to positive correlations between tests and unequal weights. It is a multi-level test in the sense that a superset of one or more significant tests is certain to be significant and conversely when the superset is non-significant, the constituent tests are certain to be non-significant. It is based on MAMML (model averaging by mean maximum likelihood), a frequentist analogue to Bayesian model averaging, and is theoretically grounded in generalized central limit theorem. For detailed examples type vignette("harmonicmeanp") after installation. Version 3.0 addresses errors in versions 1.0 and 2.0 that led function p.hmp to control the familywise error rate only in the weak sense, rather than the strong sense as intended.

Makes it easy to extract and combine variables from the HILDA (Household, Income and Labour Dynamics in Australia) survey maintained by the Melbourne Institute <https://melbourneinstitute.unimelb.edu.au/hilda>.

The healthyverse is a set of packages that work in harmony because they share common data representations and API design. This package is designed to make it easy to install and load multiple healthyverse packages in a single step.

This package provides tools to model, compare, and visualize populations of taxonomic tree objects.

Compute house price indexes and series using a variety of different methods and models common through the real estate literature. Evaluate index goodness based on accuracy, volatility and revision statistics. Background on basic model construction for repeat sales models can be found at: Case and Quigley (1991) <https://ideas.repec.org/a/tpr/restat/v73y1991i1p50-58.html> and for hedonic pricing models at: Bourassa et al (2006) <doi:10.1016/j.jhe.2006.03.001>. The package author's working paper on the random forest approach to house price indexes can be found at: <http://www.github.com/andykrause/hpi_research>.

Implementation of characteristic palettes inspired in the Wizarding World and the Harry Potter movie franchise.

Offers methods for visualizing, modelling, and forecasting high-dimensional functional time series, also known as functional panel data. Documentation about hdftsa is provided via the paper by Cristian F. Jimenez-Varon, Ying Sun and Han Lin Shang (2024, <doi:10.1080/10618600.2024.2319166>).

This package provides tools for estimating sample sizes primarily based on heritability, while also considering additional parameters such as statistical power and fold change. The package normalizes heritability values according to trait-specific heritability and classification to enhance accuracy in sample size estimation.

This package provides methods for analysing and forecasting hierarchical and grouped time series. The available forecast methods include bottom-up, top-down, optimal combination reconciliation (Hyndman et al. 2011) <doi:10.1016/j.csda.2011.03.006>, and trace minimization reconciliation (Wickramasuriya et al. 2018) <doi:10.1080/01621459.2018.1448825>.

This package provides a shiny application, which allows you to perform single- and multi-omics analyses using your own omics datasets. After the upload of the omics datasets and a metadata file, single-omics is performed for feature selection and dataset reduction. These datasets are used for pairwise- and multi-omics analyses, where automatic tuning is done to identify correlations between the datasets - the end goal of the recommended Holomics workflow. Methods used in the package were implemented in the package mixomics by Florian Rohart,Benoît Gautier,Amrit Singh,Kim-Anh Lê Cao (2017) <doi:10.1371/journal.pcbi.1005752> and are described there in further detail.

Hospital data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative hospital data. Some of these include average length of stay, readmission rates, average net pay amounts by service lines just to name a few. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything.

This package provides utilities for encoding and decoding coordinates to/from Hilbert curves based on the iterative encoding implementation described in Chen et al. (2006) <doi:10.1002/spe.793>.