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 developed a two-dimensional version of High-Throughput GoMiner ('HTGM2D'). I generate a 2D heat map whose axes are any two of BP, MF, or CC, and the value within a picture element of the heat map reflects the Jaccard metric p-value for the number of genes in common for the corresponding pair.

Quantifying similarity between high-dimensional single cell samples is challenging, and usually requires some simplifying hypothesis to be made. By transforming the high dimensional space into a high dimensional grid, the number of cells in each sub-space of the grid is characteristic of a given sample. Using a Hilbert curve each sample can be visualized as a simple density plot, and the distance between samples can be calculated from the distribution of cells using the Jensen-Shannon distance. Bins that correspond to significant differences between samples can identified using a simple bootstrap procedure.

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>.

This package provides functions to download, parse, and tidy statistical data published by HM Revenue and Customs ('HMRC') on GOV.UK'. Returns annotated hmrc_tbl data frames with provenance metadata (source URL, fetch time, vintage, cell methods) for reproducible fiscal research. Covers monthly tax receipts (41 tax heads from 2008), VAT (from 1973), fuel duties (from 1990), tobacco duties (from 1991), annual Corporation Tax receipts, stamp duty, research and development tax credit statistics (from 2000), tax gap estimates, Income Tax liabilities by income range, and monthly property transaction counts. File URLs are resolved at runtime via the GOV.UK Content API <https://www.gov.uk/api/content>, so data is always current without hardcoded URLs. Files are cached locally between sessions.

This package implements hierarchical Bayesian inference, robust frequentist inference, and distribution-free inference for dependent and unbalanced data under strong-mixing conditions. Supports triangular-array, weighted-sum and mixingale convergence regimes with Whittle and composite likelihoods, heteroskedasticity-and-autocorrelation-consistent variance estimation, block bootstrap with automatic block length, fixed-bandwidth HAR inference, adaptive conformal prediction, Bayesian decision under Region of Practical Equivalence, bridge-sampling Bayes factors, and predictive comparison via the Widely Applicable Information Criterion and leave-future-out cross-validation. Methods follow Andrews (1991) <doi:10.2307/2938229>, Kiefer and Vogelsang (2005) <doi:10.1017/S0266466605050565>, Patton, Politis and White (2009) <doi:10.1080/07474930802459016>, Vehtari, Gelman and Gabry (2017) <doi:10.1007/s11222-016-9696-4>, Kruschke (2018) <doi:10.1177/2515245918771304>, and Gibbs and Candes (2021) <doi:10.48550/arXiv.2106.00170>.

Implementation of selected high-dimensional statistical and econometric methods for estimation and inference. Efficient estimators and uniformly valid confidence intervals for various low-dimensional causal/ structural parameters are provided which appear in high-dimensional approximately sparse models. Including functions for fitting heteroscedastic robust Lasso regressions with non-Gaussian errors and for instrumental variable (IV) and treatment effect estimation in a high-dimensional setting. Moreover, the methods enable valid post-selection inference and rely on a theoretically grounded, data-driven choice of the penalty. Chernozhukov, Hansen, Spindler (2016) <arXiv:1603.01700>.

Generates synthetic electronic health record data, including patients, encounters, vitals, laboratory results, medications, procedures, and allergies. The package supports optional SARS-focused and computed tomography (CT) research views and export to CSV, SQLite, and Excel formats for research and development workflows.

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

This package provides a tool for Hierarchical Climate Regionalization applicable to any correlation-based clustering. It adds several features and a new clustering method (called, regional linkage) to hierarchical clustering in R ('hclust function in stats library): data regridding, coarsening spatial resolution, geographic masking, contiguity-constrained clustering, data filtering by mean and/or variance thresholds, data preprocessing (detrending, standardization, and PCA), faster correlation function with preliminary big data support, different clustering methods, hybrid hierarchical clustering, multivariate clustering (MVC), cluster validation, visualization of regionalization results, and exporting region map and mean timeseries into NetCDF-4 file. The technical details are described in Badr et al. (2015) <doi:10.1007/s12145-015-0221-7>.

This package creates and plots 2D and 3D hive plots. Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned. The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/). Keywords: networks, food webs, linnet, systems biology, bioinformatics.

This package implements the method developed by Cao and Kosorok (2011) for the significance analysis of thousands of features in high-dimensional biological studies. It is an asymptotically valid data-driven procedure to find critical values for rejection regions controlling the k-familywise error rate, false discovery rate, and the tail probability of false discovery proportion.

Reporting heritability estimates is an important to quantitative genetics studies and breeding experiments. Here we provide functions to calculate various broad-sense heritabilities from asreml and lme4 model objects. All methods we have implemented in this package have extensively discussed in the article by Schmidt et al. (2019) <doi:10.1534/genetics.119.302134>.

This package provides easy access to essential climate change datasets to non-climate experts. Users can download the latest raw data from authoritative sources and view it via pre-defined ggplot2 charts. Datasets include atmospheric CO2, methane, emissions, instrumental and proxy temperature records, CMIP6 projections, sea levels, Arctic/Antarctic sea-ice, Hurricanes, Wildfires, and Paleoclimate data. Sources include: NOAA Mauna Loa Laboratory <https://gml.noaa.gov/ccgg/trends/data.html>, Global Carbon Project <https://www.globalcarbonproject.org/carbonbudget/>, NASA GISTEMP <https://data.giss.nasa.gov/gistemp/>, National Snow and Sea Ice Data Center <https://nsidc.org/home>, CSIRO <https://research.csiro.au/slrwavescoast/sea-level/measurements-and-data/sea-level-data/>, NOAA Laboratory for Satellite Altimetry <https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/> and HURDAT Atlantic Hurricane Database <https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html>, Vostok Paleo carbon dioxide and temperature data: <doi:10.3334/CDIAC/ATG.009>.

Comfortable ways to work with hyperspectral data sets. I.e. spatially or time-resolved spectra, or spectra with any other kind of information associated with each of the spectra. The spectra can be data as obtained in XRF, UV/VIS, Fluorescence, AES, NIR, IR, Raman, NMR, MS, etc. More generally, any data that is recorded over a discretized variable, e.g. absorbance = f(wavelength), stored as a vector of absorbance values for discrete wavelengths is suitable.

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

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>).

This package provides a set of objects and functions for Bayes Linear emulation and history matching. Core functionality includes automated training of emulators to data, diagnostic functions to ensure suitability, and a variety of proposal methods for generating waves of points. For details on the mathematical background, there are many papers available on the topic (see references attached to function help files or the below references); for details of the functions in this package, consult the manual or help files. Iskauskas, A, et al. (2024) <doi:10.18637/jss.v109.i10>. Bower, R.G., Goldstein, M., and Vernon, I. (2010) <doi:10.1214/10-BA524>. Craig, P.S., Goldstein, M., Seheult, A.H., and Smith, J.A. (1997) <doi:10.1007/978-1-4612-2290-3_2>.

Fit an array of decision-making tasks with computational models in a hierarchical Bayesian framework. Can perform hierarchical Bayesian analysis of various computational models with a single line of coding (Ahn et al., 2017) <doi:10.1162/CPSY_a_00002>.

Enables high dimensional statistical modeling using Bayesian inference and provides a probabilistic programming language for high dimensional problems. See Kleinsasser (2026) <doi:10.5281/zenodo.22309216>.

Factor models have been widely applied in areas such as economics and finance, and the well-known heavy-tailedness of macroeconomic/financial data should be taken into account when conducting factor analysis. We propose two algorithms to do robust factor analysis by considering the Huber loss. One is based on minimizing the Huber loss of the idiosyncratic error's L2 norm, which turns out to do Principal Component Analysis (PCA) on the weighted sample covariance matrix and thereby named as Huber PCA. The other one is based on minimizing the element-wise Huber loss, which can be solved by an iterative Huber regression algorithm. In this package we also provide the code for traditional PCA, the Robust Two Step (RTS) method by He et al. (2022) and the Quantile Factor Analysis (QFA) method by Chen et al. (2021) and He et al. (2023).

Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and hole detection. Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling.

It is used to travel graphs, by using DFS and BFS to get the path from node to each leaf node. Depth first traversal(DFS) is a recursive algorithm for searching all the vertices of a graph or tree data structure. Traversal means visiting all the nodes of a graph. Breadth first traversal(BFS) algorithm is used to search a tree or graph data structure for a node that meets a set of criteria. It starts at the treeâ s root or graph and searches/visits all nodes at the current depth level before moving on to the nodes at the next depth level. Also, it provides the matrix which is reachable between each node. Implement reference about Baruch Awerbuch (1985) <doi:10.1016/0020-0190(85)90083-3>.

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.

Harriet was Charles Darwin's pet tortoise (possibly). harrietr implements some function to manipulate distance matrices and phylogenetic trees to make it easier to plot with ggplot2 and to manipulate using tidyverse tools.