This package provides functions are designed to facilitate access to and utility with large scale, publicly available environmental data in R. The package contains functions for downloading raw data files from web URLs (download_data()), processing the raw data files into clean spatial objects (process_covariates()), and extracting values from the spatial data objects at point and polygon locations (calculate_covariates()). These functions call a series of source-specific functions which are tailored to each data sources/datasets particular URL structure, data format, and spatial/temporal resolution. The functions are tested, versioned, and open source and open access. For sum_edc() method details, see Messier, Akita, and Serre (2012) <doi:10.1021/es203152a>.

An interface for performing all stages of ADMIXTOOLS analyses (<https://github.com/dreichlab/admixtools>) entirely from R. Wrapper functions (D, f4, f3, etc.) completely automate the generation of intermediate configuration files, run ADMIXTOOLS programs on the command-line, and parse output files to extract values of interest. This allows users to focus on the analysis itself instead of worrying about low-level technical details. A set of complementary functions for processing and filtering of data in the EIGENSTRAT format is also provided.

This package performs the two-sample Ansariâ Bradley test (Ansari & Bradley, 1960 <https://www.jstor.org/stable/2237814>) for univariate, distinct data in the presence of missing values, as described in Zeng et al. (2025) <doi:10.48550/arXiv.2509.20332>. This method does not make any assumptions about the missingness mechanisms and controls the Type I error regardless of the missing values by taking all possible missing values into account.

Converts legacy microscopy video formats (H.264/H.265, AVI/MJPEG, TIFF stacks) to the modern AV1 codec with minimal quality loss. Typical use cases include compressing large TIFF stacks from confocal microscopy and time-lapse experiments from hundreds of gigabytes to manageable sizes, re-encoding MP4 files exported from CellProfiler', ImageJ'/'Fiji', and microscope software with approximately 2x better compression at the same visual quality, and converting legacy AVI (MJPEG) and H.265 recordings to a single patent-free format suited for long-term archival. Automatically selects the best available backend: GPU hardware acceleration via Vulkan VK_KHR_VIDEO_ENCODE_AV1 or VAAPI (tested on AMD RDNA4; bundled headers, builds with any Vulkan SDK >= 1.3.275), with automatic fallback to CPU encoding through FFmpeg and SVT-AV1'. User controls quality via a single CRF parameter; each backend adapts automatically (CPU and Vulkan use CRF directly, VAAPI targets 55 percent of input bitrate). TIFF stacks use near-lossless CRF 5 by default, with optional proportional scaling via tiff_scale (multiplier or bounding box, aspect ratio always preserved). Small frames are automatically scaled up to meet hardware encoder minimums. Audio tracks are preserved automatically. Provides a simple R API for batch conversion of entire experiment folders.

An isotope natural abundance correction algorithm that is needed especially for high resolution mass spectrometers. Supports correction for 13C, 2H and 15N. Su X, Lu W and Rabinowitz J (2017) <doi:10.1021/acs.analchem.7b00396>.

This package provides a suite of functions for analyzing sequences of events. Users can generate and code sequences based on predefined rules, with a special focus on the identification of sequences coded as ABA (when one element appears, followed by a different one, and then followed by the first). Additionally, the package offers the ability to calculate the length of consecutive ABA'-coded sequences sharing common elements. The methods implemented in this package are based on the work by Ziembowicz, K., Rychwalska, A., & Nowak, A. (2022). <doi:10.1177/10464964221118674>.

Actuarial reports are prepared for the last day of a specific period, such as a month, a quarter or a year. Actuarial models assume that certain events happen at the beginning or end of periods. The package contains functions to easily refer to the first or last (working) day within a specific period relative to a base date to facilitate actuarial reporting and to compare results.

Utilities designed to make the analysis of field trials easier and more accessible for everyone working in plant breeding. It provides a simple and intuitive interface for conducting single and multi-environmental trial analysis, with minimal coding required. Whether you're a beginner or an experienced user, agriutilities will help you quickly and easily carry out complex analyses with confidence. With built-in functions for fitting Linear Mixed Models, agriutilities is the ideal choice for anyone who wants to save time and focus on interpreting their results. Some of the functions require the R package asreml for the ASReml software, this can be obtained upon purchase from VSN international <https://vsni.co.uk/software/asreml-r/>.

This package implements a web-based graphics device for animated visualisations. Modelled on the base syntax, it extends the base graphics functions to support frame-by-frame animation and keyframes animation. The target use cases are real-time animated visualisations, including agent-based models, dynamical systems, and animated diagrams. The generated visualisations can be deployed as GIF images / MP4 videos, as Shiny apps (with interactivity) or as HTML documents through embedding into R Markdown documents.

Interactive graphical user interface (GUI) for the package AdhereR', allowing the user to access different data sources, to explore the patterns of medication use therein, and the computation of various measures of adherence. It is implemented using Shiny and HTML/CSS/JavaScript.

Add-on package to the airGR package that simplifies its use and is aimed at being used for teaching hydrology. The package provides 1) three functions that allow to complete very simply a hydrological modelling exercise 2) plotting functions to help students to explore observed data and to interpret the results of calibration and simulation of the GR ('Génie rural') models 3) a Shiny graphical interface that allows for displaying the impact of model parameters on hydrographs and models internal variables.

Retrieves open source airport data and provides tools to look up information, translate names into codes and vice-verse, as well as some basic calculation functions for measuring distances. Data is licensed under the Open Database License.

This package provides tools to simulate alphanumeric alleles, impute genetic missing data and reconstruct non-recombinant haplotypes from pedigree databases in a deterministic way. Allelic simulations can be implemented taking into account many factors (such as number of families, markers, alleles per marker, probability and proportion of missing genotypes, recombination rate, etc). Genotype imputation can be used with simulated datasets or real databases (previously loaded in .ped format). Haplotype reconstruction can be carried out even with missing data, since the program firstly imputes each family genotype (without a reference panel), to later reconstruct the corresponding haplotypes for each family member. All this considering that each individual (due to meiosis) should unequivocally have two alleles per marker (one inherited from each parent) and thus imputation and reconstruction results can be deterministically calculated.

This package implements the Analytic Hierarchy Process (AHP) method using Gaussian normalization (AHPGaussian) to derive the relative weights of the criteria and alternatives. It also includes functions for visualizing the results and generating graphical outputs. Method as described in: dos Santos, Marcos (2021) <doi:10.13033/ijahp.v13i1.833>.

This package provides methods to analyse the stability of non-deterministic prediction models. Prediction stability is quantified either as data-based prediction stability (phi) or as model-based prediction stability (psi). The package implements measures for categorical, ordinal, and metric predictions based on repeated model fitting and corresponding predictions. Methods are based on Lange et al. (2025) <doi:10.1186/s12859-025-06097-1>.

This package implements the allan variance and allan variance linear regression estimator for latent time series models. More details about the method can be found, for example, in Guerrier, S., Molinari, R., & Stebler, Y. (2016) <doi:10.1109/LSP.2016.2541867>.

This package implements the full suite of simulation, visualization, and analysis tools for exploring the mathematical isomorphisms between ant colony decision-making and three major paradigms of machine learning: random forests (Part I: variance reduction through decorrelation), boosting (Part II: bias reduction through adaptive recruitment), and neural networks (Part III: gradient-based generational learning). Accompanies the trilogy "Isomorphic Functionalities between Ant Colony and Ensemble Learning" (Fokoué, Babbitt, and Levental, 2026, <doi:10.48550/arXiv.2603.20328>, <doi:10.48550/arXiv.2604.00038>).

This package provides a novel parametrization of log transformation and a shift parameter to automate the transformation process are proposed in R package AutoTransQF based on Feng et al. (2016). Please read Feng et al. (2016) <doi:10.1002/sta4.104> for more details of the method.

Compute an anomaly score for multivariate time series based on the k-nearest neighbors algorithm. Different computations of distances between time series are provided.

This package provides tools for reproducible, offline analysis of endurance-training data exported from Strava'. Provides data import, quality-control, cohort-reference, and visualization helpers for sports-science indicators including acute:chronic workload ratio, aerobic efficiency, cardiovascular decoupling, exposure, and personal-best profiles.

We extend existing gene enrichment tests to perform adverse event enrichment analysis. Unlike the continuous gene expression data, adverse event data are counts. Therefore, adverse event data has many zeros and ties. We propose two enrichment tests. One is a modified Fisher's exact test based on pre-selected significant adverse events, while the other is based on a modified Kolmogorov-Smirnov statistic. We add Covariate adjustment to improve the analysis."Adverse event enrichment tests using VAERS" Shuoran Li, Lili Zhao (2020) <doi:10.48550/arXiv.2007.02266>.

Calculate ActiGraph counts from the X, Y, and Z axes of a triaxial accelerometer. This work was inspired by Neishabouri et al. who published the article "Quantification of Acceleration as Activity Counts in ActiGraph Wearables" on February 24, 2022. The link to the article (<https://pubmed.ncbi.nlm.nih.gov/35831446>) and python implementation of this code (<https://github.com/actigraph/agcounts>).

Lets you open a fixed-width ASCII file (.txt or .dat) that has an accompanying setup file (.sps or .sas). These file combinations are sometimes referred to as .txt+.sps, .txt+.sas, .dat+.sps, or .dat+.sas. This will only run in a txt-sps or txt-sas pair in which the setup file contains instructions to open that text file. It will NOT open other text files, .sav, .sas, or .por data files. Fixed-width ASCII files with setup files are common in older (pre-2000) government data.

This package provides a collection of tools that support data splitting, predictive modeling, and model evaluation. A typical function is to split a dataset into a training dataset and a test dataset. Then compare the data distribution of the two datasets. Another feature is to support the development of predictive models and to compare the performance of several predictive models, helping to select the best model.