This package provides a simple and time saving multiple linear regression function (OLS) with interpretation, optional bootstrapping, effect size calculation and all tested requirements.

Audio interactivity within shiny applications using howler.js'. Enables the status of the audio player to be sent from the UI to the server, and events such as playing and pausing the audio can be triggered from the server.

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.

Efficient implementations of the following multiple changepoint detection algorithms: Efficient Sparsity Adaptive Change-point estimator by Moen, Glad and Tveten (2023) <doi:10.48550/arXiv.2306.04702> , Informative Sparse Projection for Estimating Changepoints by Wang and Samworth (2017) <doi:10.1111/rssb.12243>, and the method of Pilliat et al (2023) <doi:10.1214/23-EJS2126>.

This package creates styled tables for data presentation. Export to HTML, LaTeX, RTF, Word', Excel', PowerPoint', typst', SVG and PNG. Simple, modern interface to manipulate borders, size, position, captions, colours, text styles and number formatting. Table cells can span multiple rows and/or columns. Includes a huxreg function to create regression tables, and quick_* one-liners to print tables to a new document.

It provides three implementations of hash tables and hash maps: 1. using std::unordered_map and std::unordered_set C++ libraries, 2. wrapping around the fastmatch package, 3. using R environment.

Shiny-App that allows to annotate vectors of texts to predefined categories by hand.

Computes the ACMIF test and Bonferroni-adjusted p-value of interaction in two-factor studies. Produces corresponding interaction plot and analysis of variance tables and p-values from several other tests of non-additivity.

This package provides a unified API for creating data visualizations across dynamic and static rendering modes. Visualizations are defined once using a specification object and can be rendered with highcharter', ggplot2', or other supported packages without modifying user code. The package supports declarative and layered workflows, reusable themes and colour palettes, optional JavaScript enhancements, export tools, and interactive exploration through shiny applications.

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.

High throughput toxicokinetics ("HTTK") is the combination of 1) chemical-specific in vitro measurements or in silico predictions and 2) generic mathematical models, to predict absorption, distribution, metabolism, and excretion by the body. HTTK methods have been described by Pearce et al. (2017) (<doi:10.18637/jss.v079.i04>) and Breen et al. (2021) (<doi:10.1080/17425255.2021.1935867>). Here we provide examples (vignettes) applying HTTK to solve various problems in bioinformatics, toxicology, and exposure science. In accordance with Davidson-Fritz et al. (2025) (<doi:10.1371/journal.pone.0321321>), whenever a new HTTK model is developed, the code to generate the figures evaluating that model is added as a new vignettte.

This package provides diagnostic functions for integrating longitudinal soil, plant and microbial observations during redox disturbance and recovery. Functions calculate stoichiometric potential oxygen demand, accessible electron capacity from explicitly supplied inventories and kinetic parameters, recovery signatures, fixed-reference domain scores, and exploratory multiblock scores with observation-coverage diagnostics. Memory is represented as a holobiont state accumulating from mineralogical, plant-acclimation and microbial-community legacies. An illustrative simulator produces closed Fe and Mn inventories alongside synthetic observations; its carbon, nitrogen, sulfur and oxygen budgets are not closed and its parameters are not calibrated to field rates. Simulation benchmarks assess agreement with a prescribed synthetic target and do not constitute empirical validation or parameter identification. Accuracy assessment is cluster-aware: intervals come from resampling whole trajectories, agreement is reported as Lin's concordance coefficient alongside correlation, and mean squared error is partitioned into bias, variance mismatch and lack of correlation. The measured quantities follow Sander, Hofstetter and Gorski (2015) <doi:10.1021/acs.est.5b00006> for mediated electrochemical determination of electron-accepting and electron-donating capacity, Kluepfel, Piepenbrock, Kappler and Sander (2014) <doi:10.1038/ngeo2084> for regeneration of electron-accepting capacity across repeated anoxic periods, Thompson, Chadwick, Rancourt and Chorover (2006) <doi:10.1016/j.gca.2005.12.005> for the increase in iron-oxide crystallinity under redox oscillation, and Keiluweit, Wanzek, Kleber, Nico and Fendorf (2017) <doi:10.1038/s41467-017-01406-6> for anaerobic microsites in otherwise aerobic soil. Agreement statistics follow Lin (1989) <doi:10.2307/2532051> and Kobayashi and Salam (2000) <doi:10.2134/agronj2000.922345x>.

This package provides a novel decision tree algorithm in the hypothesis testing framework. The algorithm examines the distribution difference between two child nodes over all possible binary partitions. The test statistic of the hypothesis testing is equivalent to the generalized energy distance, which enables the algorithm to be more powerful in detecting the complex structure, not only the mean difference. It is applicable for numeric, nominal, ordinal explanatory variables and the response in general metric space of strong negative type. The algorithm has superior performance compared to other tree models in type I error, power, prediction accuracy, and complexity.

Higher order likelihood inference is a promising approach for analyzing small sample size data. The holi package provides web applications for higher order likelihood inference. It currently supports linear, logistic, and Poisson generalized linear models through the rstar_glm() function, based on Pierce and Bellio (2017) <doi:10.1111/insr.12232> and likelihoodAsy'. The package offers two main features: LA_rstar(), which launches an interactive shiny application allowing users to fit models with rstar_glm() through their web browser, and sim_rstar_glm_pgsql(), which streamlines the process of launching a web-based shiny simulation application that saves results to a user-created PostgreSQL database.

Cellular responses to perturbations are highly heterogeneous and depend largely on the initial state of cells. Connecting post-perturbation cells via cellular trajectories to untreated cells (e.g. by leveraging metabolic labeling information) enables exploitation of intercellular heterogeneity as a combined knock-down and overexpression screen to identify pathway modulators, termed Heterogeneity-seq (see Berg et al <doi:10.1101/2024.10.28.620481>). This package contains functions to generate cellular trajectories based on scSLAM-seq (single-cell, thiol-(SH)-linked alkylation of RNA for metabolic labelling sequencing) time courses, functions to identify pathway modulators and to visualize the results.

This package implements hierarchical conformal prediction for clustered data with missing responses. The method uses repeated cluster-level splitting and within-cluster subsampling to accommodate dependence, and inverse-probability weighting to correct distribution shift induced by missingness. Conditional densities are estimated by inverting fitted conditional quantiles (linear quantile regression or quantile regression forests), and p-values are aggregated across resampling and splitting steps using the Cauchy combination test.

Build better balance in causal inference models. halfmoon helps you assess propensity score models for balance between groups using metrics like standardized mean differences and visualization techniques like mirrored histograms. halfmoon supports both weighting and matching techniques.

This package provides a self-contained, static build of the HDF5 (Hierarchical Data Format 5) C library (release 2.2.0) for R package developers. Designed for use in the LinkingTo field, it enables zero-dependency integration by building the library entirely from source during installation. Additionally, it compiles and internally links a comprehensive suite of advanced compression filters and their HDF5 plugins (Zstd, LZ4, Blosc/Blosc2, Snappy, ZFP, Bzip2, LZF, Bitshuffle, szip, and gzip). These plugins are integrated out-of-the-box, allowing downstream packages to utilize high-performance compression directly through the standard HDF5 API while keeping the underlying third-party headers fully encapsulated. HDF5 is developed by The HDF Group <https://www.hdfgroup.org/>.

This package provides a scalable implementation of the highly adaptive lasso algorithm, including routines for constructing sparse matrices of basis functions of the observed data, as well as a custom implementation of Lasso regression tailored to enhance efficiency when the matrix of predictors is composed exclusively of indicator functions. For ease of use and increased flexibility, the Lasso fitting routines invoke code from the glmnet package by default. The highly adaptive lasso was first formulated and described by MJ van der Laan (2017) <doi:10.1515/ijb-2015-0097>, with practical demonstrations of its performance given by Benkeser and van der Laan (2016) <doi:10.1109/DSAA.2016.93>. This implementation of the highly adaptive lasso algorithm was described by Hejazi, Coyle, and van der Laan (2020) <doi:10.21105/joss.02526>.

In some cases you will have data in a histogram format, where you have a vector of all possible observations, and a vector of how many times each observation appeared. You could expand this into a single 1D vector, but this may not be advisable if the counts are extremely large. HistDat allows for the calculation of summary statistics without the need for expanding your data.

Statistical functions used in the French HydroPortail <https://hydro.eaufrance.fr/>. This includes functions to estimate distributions, quantile curves and uncertainties, along with various other utilities. Technical details are available (in French) in Renard (2016) <https://hal.inrae.fr/hal-02605318>.

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>.

User-friendly and fast set of functions for estimating parameters of hierarchical Bayesian species distribution models (Latimer and others 2006 <doi:10.1890/04-0609>). Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results.

This package provides a broad collection of datasets focused on health, biomechanics, and human motion. It includes clinical, physiological, and kinematic information from diverse sources, covering aspects such as surgery outcomes, vital signs, rheumatoid arthritis, osteoarthritis, accelerometry, gait analysis, motion sensing, and biomechanics experiments. Designed for researchers, analysts, and students, the package facilitates exploration and analysis of data related to health monitoring, physical activity, and rehabilitation.