Write executable specifications in a natural language that describes how your code should behave. Write specifications in feature files using Gherkin language and execute them using functions implemented in R. Use them as an extension to your testthat tests to provide a high level description of how your code works.

Auto, Cross and Multi-dimensional recurrence quantification analysis. Different methods for computing recurrence, cross vs. multidimensional or profile iti.e., only looking at the diagonal recurrent points, as well as functions for optimization and plotting are proposed. in-depth measures of the whole cross-recurrence plot, Please refer to Coco and others (2021) <doi:10.32614/RJ-2021-062>, Coco and Dale (2014) <doi:10.3389/fpsyg.2014.00510> and Wallot (2018) <doi: 10.1080/00273171.2018.1512846> for further details about the method.

This package provides interface to the Copernicus Data Space Ecosystem API <https://dataspace.copernicus.eu/analyse/apis>, mainly for searching the catalog of available data from Copernicus Sentinel missions and obtaining the images for just the area of interest based on selected spectral bands. The package uses the Sentinel Hub REST API interface <https://dataspace.copernicus.eu/analyse/apis/sentinel-hub> that provides access to various satellite imagery archives. It allows you to access raw satellite data, rendered images, statistical analysis, and other features. This package is in no way officially related to or endorsed by Copernicus.

Client for CKAN API (<https://ckan.org/>). Includes interface to CKAN APIs for search, list, show for packages, organizations, and resources. In addition, provides an interface to the datastore API.

This package performs survival analysis using general non-linear models. Risk models can be the sum or product of terms. Each term is the product of exponential/linear functions of covariates. Additionally sub-terms can be defined as a sum of exponential, linear threshold, and step functions. Cox Proportional hazards <https://en.wikipedia.org/wiki/Proportional_hazards_model>, Poisson <https://en.wikipedia.org/wiki/Poisson_regression>, and Fine-Gray competing risks <https://www.publichealth.columbia.edu/research/population-health-methods/competing-risk-analysis> regression are supported. This work was sponsored by NASA Grants 80NSSC19M0161 and 80NSSC23M0129 through a subcontract from the National Council on Radiation Protection and Measurements (NCRP). The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, ACI-1440548, CHE-1726332, and NIH P20GM113109.

An interface to the cycle routing/data services provided by CycleStreets', a not-for-profit social enterprise and advocacy organisation. The application programming interfaces (APIs) provided by CycleStreets are documented at (<https://www.cyclestreets.net/api/>). The focus of this package is the journey planning API, which aims to emulate the routes taken by a knowledgeable cyclist. An innovative feature of the routing service of its provision of fastest, quietest and balanced profiles. These represent routes taken to minimise time, avoid traffic and compromise between the two, respectively.

This package provides functions for estimating and reporting multi-year averages and corresponding confidence intervals and distributions. A potential use case is reporting the chemical and ecological status of surface waters according to the European Water Framework Directive.

Generate synthetic station-based monthly climate time-series including temperature and rainfall, export to Network Common Data Form (NetCDF), and provide visualization helpers for climate workflows. The approach is inspired by statistical weather generator concepts described in Wilks (1999) <doi:10.1016/S0168-1923(99)00037-4> and Richardson (1981) <doi:10.1029/WR017i001p00182>.

Uses non-linear regression to statistically compare two circadian rhythms. Groups are only compared if both are rhythmic (amplitude is non-zero). Performs analyses regarding mesor, phase, and amplitude, reporting on estimates and statistical differences, for each, between groups. Details can be found in Parsons et al (2020) <doi:10.1093/bioinformatics/btz730>.

This package implements a kernel-based association test for copy number variation (CNV) aggregate analysis in a certain genomic region (e.g., gene set, chromosome, or genome) that is robust to the within-locus and across-locus etiological heterogeneity, and bypass the need to define a "locus" unit for CNVs. Brucker, A., et al. (2020) <doi:10.1101/666875>.

The goal of cvsem is to provide functions that allow for comparing Structural Equation Models (SEM) using cross-validation. Users can specify multiple SEMs using lavaan syntax. cvsem computes the Kullback Leibler (KL) Divergence between 1) the model implied covariance matrix estimated from the training data and 2) the sample covariance matrix estimated from the test data described in Cudeck, Robert & Browne (1983) <doi:10.18637/jss.v048.i02>. The KL Divergence is computed for each of the specified SEMs allowing for the models to be compared based on their prediction errors.

Choose the appropriate map projection for a given application, visualise the resulting distortion, and georeference data from unknown projections. The full functionalities of the package are described in Pozzi et al. (2026) <doi:10.31223/X5K17P> (pre-print).

Package for CShapes 2.0, a GIS dataset of country borders (1886-today). Includes functions for data extraction and the computation of distance matrices and -lists.

Multivariate random forests with compositional responses and Euclidean predictors is performed. The compositional data are first transformed using the additive log-ratio transformation, or the alpha-transformation of Tsagris, Preston and Wood (2011), <doi:10.48550/arXiv.1106.1451>, and then the multivariate random forest of Rahman R., Otridge J. and Pal R. (2017), <doi:10.1093/bioinformatics/btw765>, is applied.

This package provides a tool that implements the clustering algorithms from mothur (Schloss PD et al. (2009) <doi:10.1128/AEM.01541-09>). clustur make use of the cluster() and make.shared() command from mothur'. Our cluster() function has five different algorithms implemented: OptiClust', furthest', nearest', average', and weighted'. OptiClust is an optimized clustering method for Operational Taxonomic Units, and you can learn more here, (Westcott SL, Schloss PD (2017) <doi:10.1128/mspheredirect.00073-17>). The make.shared() command is always applied at the end of the clustering command. This functionality allows us to generate and create clustering and abundance data efficiently.

Fork of calendR R package to generate ready to print calendars with ggplot2 (see <https://r-coder.com/calendar-plot-r/>) with additional features (backwards compatible). calendRio provides a calendR() function that serves as a drop-in replacement for the upstream version but allows for additional parameters unlocking extra functionality.

This package provides functions for performing quick observations or evaluations of data, including a variety of ways to list objects by size, class, etc. The functions seqle and reverse.seqle mimic the base rle but can search for linear sequences. The function splatnd allows the user to generate zero-argument commands without the need for makeActiveBinding . Functions provided to convert from any base to any other base, and to find the n-th greatest max or n-th least min. In addition, functions which mimic Unix shell commands, including head', tail ,'pushd ,and popd'. Various other goodies included as well.

The Codemeta Project defines a JSON-LD format for describing software metadata, as detailed at <https://codemeta.github.io>. This package provides core utilities to generate this metadata with a minimum of dependencies.

Agreement of continuously scaled measurements made by two techniques, devices or methods is usually evaluated by the well-established Bland-Altman analysis or plot. Conditional method agreement trees (COAT), proposed by Karapetyan, Zeileis, Henriksen, and Hapfelmeier (2025) <doi:10.1093/jrsssc/qlae077>, embed the Bland-Altman analysis in the framework of recursive partitioning to explore heterogeneous method agreement in dependence of covariates. COAT can also be used to perform a Bland-Altman test for differences in method agreement.

This package provides a suite of computer model test functions that can be used to test and evaluate algorithms for Bayesian (also known as sequential) optimization. Some of the functions have known functional forms, however, most are intended to serve as black-box functions where evaluation requires running computer code that reveals little about the functional forms of the objective and/or constraints. The primary goal of the package is to provide users (especially those who do not have access to real computer models) a source of reproducible and shareable examples that can be used for benchmarking algorithms. The package is a living repository, and so more functions will be added over time. For function suggestions, please do contact the author of the package.

This package provides automated methods for downloading, recoding, and merging selected years of the Current Population Survey's Voting and Registration Supplement <https://www.nber.org/research/data/current-population-survey-cps-supplements-voting-and-registration>, a large N national survey about registration, voting, and non-voting in United States federal elections. Provides documentation for appropriate use of sample weights to generate statistical estimates, drawing from Hur & Achen (2013) <doi:10.1093/poq/nft042> and McDonald (2018) <https://www.electproject.org/election-data/voter-turnout-data>.

Tests, utilities, and case studies for analyzing significance in clustered binary matched-pair data. The central function clust.bin.pair uses one of several tests to calculate a Chi-square statistic. Implemented are the tests Eliasziw (1991) <doi:10.1002/sim.4780101211>, Obuchowski (1998) <doi:10.1002/(SICI)1097-0258(19980715)17:13%3C1495::AID-SIM863%3E3.0.CO;2-I>, Durkalski (2003) <doi:10.1002/sim.1438>, and Yang (2010) <doi:10.1002/bimj.201000035> with McNemar (1947) <doi:10.1007/BF02295996> included for comparison. The utility functions nested.to.contingency and paired.to.contingency convert data between various useful formats. Thyroids and psychiatry are the canonical datasets from Obuchowski and Petryshen (1989) <doi:10.1016/0165-1781(89)90196-0> respectively.

Systematically Run R checks against multiple packages. Checks are run in parallel with strategies to minimize dependency installation. Provides out of the box interface for running reverse dependency check.

This package provides functions for calculating the conditional power for different models in survival time analysis within randomized clinical trials with two different treatments to be compared and survival as an endpoint.