Helper function to install packages for R using an external requirements.txt or a string containing diverse packages from several resources like Github or CRAN.

This package provides Java graphical user interfaces for viewing, manipulating and plotting graphs. Graphs may be directed or undirected.

OpenRefine (formerly Google Refine') is a popular, open source data cleaning software. This package enables users to programmatically trigger data transfer between R and OpenRefine'. Available functionality includes project import, export and deletion.

Provide helper functions for package developers to create active bindings that looks like data embedded in the package, but are downloaded from remote sources.

This package provides a dependency-light OpenTimelineIO (OTIO) <https://github.com/AcademySoftwareFoundation/OpenTimelineIO> document layer in pure R. Provides constructors for the OTIO object model (timelines, tracks, clips, gaps, media references, rational times and time ranges), functional builders that return new objects, and readers and writers for canonical .otio files through jsonlite'. The optional RcppOTIO package validates output against the real OpenTimelineIO C++ library. No compiled code.

This package provides datasets related to the Star Trek fictional universe and functions for working with the data. The package also provides access to real world datasets based on the televised series and other related licensed media productions. It interfaces with the Star Trek API (STAPI) (<http://stapi.co/>), Memory Alpha (<https://memory-alpha.fandom.com/wiki/Portal:Main>), and Memory Beta (<https://memory-beta.fandom.com/wiki/Main_Page>) to retrieve data, metadata and other information relating to Star Trek. It also contains several local datasets covering a variety of topics. The package also provides functions for working with data from other Star Trek-related R data packages containing larger datasets not stored in rtrek'.

Search, composite, and download Google Earth Engine imagery with reticulate bindings for the Python module geedim by Dugal Harris. Read the geedim documentation here: <https://geedim.readthedocs.io/>. Wrapper functions are provided to make it more convenient to use geedim to download images larger than the Google Earth Engine size limit <https://developers.google.com/earth-engine/apidocs/ee-image-getdownloadurl>. By default the "High Volume" API endpoint <https://developers.google.com/earth-engine/cloud/highvolume> is used to download data and this URL can be customized during initialization of the package.

This package provides tools for fitting, diagnosing, and analyzing tie-oriented and actor-oriented relational event models, under both frequentist and Bayesian approaches. The package supports tie-oriented modeling (Butts, 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) and an actor-oriented modeling framework (Stadtfeld et al., 2017, <doi:10.15195/v4.a14>), with additional model diagnostics and goodness-of-fit tools. Interfaces to estimation backends provide a range of extensions: random-effects (frailty) relational event models capturing sender, receiver, and dyadic heterogeneity (Juozaitiene & Wit 2024, <doi:10.1007/s11336-024-09952-x>; Mulder & Hoff, 2024, <doi:10.1214/24-AOAS1885>), finite mixture and dyadic latent class models for unobserved dyadic heterogeneity (Lakdawala et al., 2026, <doi:10.1016/j.socnet.2026.06.006>), penalized estimation via the lasso, ridge, and elastic net (Tibshirani, R., 1996, <doi:10.1111/j.2517-6161.1996.tb02080.x>; Karimova et al., 2023, <doi:10.1016/j.socnet.2023.02.006>), and approximate Bayesian regularization (Karimova et al., 2025, <doi:10.1016/j.jmp.2025.102925>). Modeling of events with a duration is also supported (Lakdawala et al., 2026, <doi:10.48550/arXiv.2602.21000>) and moving window relational event models (Mulder & Leenders, 2019, <doi:10.1016/j.chaos.2018.11.027>; Meijerink et al., 2023, <doi:10.1371/journal.pone.0272309>).

The Radiant Design menu includes interfaces for design of experiments, sampling, and sample size calculation. The application extends the functionality in radiant.data'.

Download large sections of GenBank <https://www.ncbi.nlm.nih.gov/genbank/> and generate a local SQL-based database. A user can then query this database using restez functions or through rentrez <https://CRAN.R-project.org/package=rentrez> wrappers.

An R interface to United States Environmental Protection Agency (EPA) Assessment, Total Maximum Daily Load (TMDL) Tracking and Implementation System ('ATTAINS') data. ATTAINS is the EPA database used to track information provided by states about water quality assessments conducted under federal Clean Water Act requirements. ATTAINS information and API information is available at <https://www.epa.gov/waterdata/attains>.

Minirhizotrons are widely used to observe and explore roots and their growth. This package provides the means to stitch images and divide them into depth layers. Please note that this R package was developed alongside the following manuscript: Stitching root scans and extracting depth layer information -- a workflow and practical examples, S. Kersting, L. Knüver, and M. Fischer. The manuscript is currently in preparation and should be citet as soon as it is available. This project was supported by the project ArtIGROW, which is a part of the WIR!-Alliance ArtIFARM â Artificial Intelligence in Farming funded by the German Federal Ministry of Research, Technology and Space (No. 03WIR4805).

Tensor Factor Models (TFM) are appealing dimension reduction tools for high-order tensor time series, and have wide applications in economics, finance and medical imaging. We propose an one-step projection estimator by minimizing the least-square loss function, and further propose a robust estimator with an iterative weighted projection technique by utilizing the Huber loss function. The methods are discussed in Barigozzi et al. (2022) <arXiv:2206.09800>, and Barigozzi et al. (2023) <arXiv:2303.18163>.

BM25 is a ranking function used by search engines to rank matching documents according to their relevance to a user's search query. This package provides a light wrapper around the BM25 rust crate for Okapi BM25 text search. For more information, see Robertson et al. (1994) <https://trec.nist.gov/pubs/trec3/t3_proceedings.html>.

This package provides an I/O interface between R data.frames and Raven DataFrames. Defines functions to both read and write DataFrame files, as well as serialize/deserialize data.frames/DataFrames.

Allows for production of Czekanowski's Diagrams with clusters. See K. Bartoszek, A. Vasterlund (2020) <doi:10.2478/bile-2020-0008> and K. Bartoszek, Y. Luo (2023) <doi:10.14708/ma.v51i2.7259>. The suggested FuzzyDBScan package (which allows for fuzzy clustering) can be obtained from <https://github.com/henrifnk/FuzzyDBScan/> (or from CRAN's Archive <https://cran.r-project.org/src/contrib/Archive/FuzzyDBScan/>).

This package provides a friendly, object oriented API for creating PowerPoint slide decks in R.

Assists for presentation and visualization of data from the Norwegian Health Quality Registries following the standardization based on the requirement specified by the National Service for Health Quality Registries. This requirement can be accessed from (<https://www.kvalitetsregistre.no/resultater-til-publisering-pa-nett>). Unfortunately the website is only available in Norwegian.

R bindings for functions from the UCR Suite by Rakthanmanon et al. (2012) <DOI:10.1145/2339530.2339576>, which enables ultrafast subsequence search for a best match under Dynamic Time Warping and Euclidean Distance.

This package implements a density-free recentered influence function (RIF) regression framework for unconditional expectiles, and embeds it in a two-sample Oaxaca-Blinder decomposition indexed continuously by the expectile level. Unlike quantile-based RIF decompositions, which require estimating an inverse density term at each quantile, the expectile RIF depends only on primitive moments of the outcome distribution and requires no density estimation, no bandwidth selection, and no kernel smoothing. The package provides expectile estimation by iteratively reweighted least squares, closed-form RIF construction, two-sample composition/structure decomposition across a grid of expectile levels, bootstrap-based inference, and plotting methods. The underlying methodology is described in Ndoye (2025), "Semi-Nonparametric Expectile RIF Regression for Distributional Decomposition," presented at the 2025 World Congress of the Econometric Society, Seoul, Korea, <https://www.econometricsociety.org/regional-activities/conference-papers/view/282/943>.

This package provides functions to complete three-dimensional rock fabric and strain analyses following the Rf Phi, Fry, and normalized Fry methods. Also allows for plotting of results and interactive 3D visualization functionality.

Defines storage standard for Read, process, and analyze intracranial electroencephalography and deep-brain stimulation in RAVE', a reproducible framework for analysis and visualization of iEEG by Magnotti, Wang, and Beauchamp, (2020, <doi:10.1016/j.neuroimage.2020.117341>). Supports brain imaging data structure (BIDS) <https://bids.neuroimaging.io> and native file structure to ingest signals from Matlab data files, hierarchical data format 5 (HDF5), European data format (EDF), BrainVision core data format (BVCDF), or BlackRock Microsystem (NEV/NSx); process images in Neuroimaging informatics technology initiative (NIfTI) and FreeSurfer formats, providing brain imaging normalization to template brain, facilitating threeBrain package for comprehensive electrode localization via YAEL (your advanced electrode localizer) by Wang, Magnotti, Zhang, and Beauchamp (2023, <doi:10.1523/ENEURO.0328-23.2023>).

Show physics, math and engineering students how an ODE solver is made and how effective R classes can be for the construction of the equations that describe natural phenomena. Inspiration for this work comes from the book on "Computer Simulations in Physics" by Harvey Gould, Jan Tobochnik, and Wolfgang Christian. Book link: <http://www.compadre.org/osp/items/detail.cfm?ID=7375>.

Indices for assessing riverscape fragmentation, including the Dendritic Connectivity Index, the Population Connectivity Index, the River Fragmentation Index, the Probability of Connectivity, and the Integral Index of connectivity. For a review, see Jumani et al. (2020) <doi:10.1088/1748-9326/abcb37> and Baldan et al. (2022) <doi:10.1016/j.envsoft.2022.105470> Functions to calculate temporal indices improvement when fragmentation due to barriers is reduced are also included.