With this package we provide an easy method to compute robust and conditional Data Envelopment Analysis (DEA), Free Disposal Hull (FDH) and Benefit of the Doubt (BOD) scores. The robust approach is based on the work of Cazals, Florens and Simar (2002) <doi:10.1016/S0304-4076(01)00080-X>. The conditional approach is based on Daraio and Simar (2007) <doi:10.1007/s11123-007-0049-3>. Besides we provide graphs to help with the choice of m. We relay on the Benchmarking package to compute the efficiency scores and on the np package to compute non parametric estimation of similarity among units.

This package provides randomization tests and graphical diagnostics for assessing randomized assignment and covariate balance for a binary treatment variable. See Branson (2021) <arXiv:1804.08760> for details.

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.

The significance of mean difference tests in clinical trials is established if at least r null hypotheses are rejected among m that are simultaneously tested. This package enables one to compute necessary sample sizes for single-step (Bonferroni) and step-wise procedures (Holm and Hochberg). These three procedures control the q-generalized family-wise error rate (probability of making at least q false rejections). Sample size is computed (for these single-step and step-wise procedures) in a such a way that the r-power (probability of rejecting at least r false null hypotheses, i.e. at least r significant endpoints among m) is above some given threshold, in the context of tests of difference of means for two groups of continuous endpoints (variables). Various types of structure of correlation are considered. It is also possible to analyse data (i.e., actually test difference in means) when these are available. The case r equals 1 is treated in separate functions that were used in Lafaye de Micheaux et al. (2014) <doi:10.1080/10543406.2013.860156>.

Data objects in R can be rendered as HTML tables using the JavaScript library ag-grid (typically via R Markdown or Shiny'). The ag-grid library has been included in this R package. The package name RagGrid is an abbreviation of R agGrid'.

An example package which shows use of NLopt functionality from C++ via Rcpp without requiring linking, and relying just on nloptr thanks to the exporting API added there by Jelmer Ypma. This package is a fully functioning, updated, and expanded version of the initial example by Julien Chiquet at <https://github.com/jchiquet/RcppArmadilloNLoptExample> also containing a large earlier pull request of mine.

Direct insertion of over 1000 symbols (e.g. currencies, letters, emojis, arrows, mathematical symbols and so on) into Rmarkdown documents and Shiny applications by incorporating HTML hex codes.

Infer log-linear Poisson Graphical Model with an auxiliary data set. Hot-deck multiple imputation method is used to improve the reliability of the inference with an auxiliary dataset. Standard log-linear Poisson graphical model can also be used for the inference and the Stability Approach for Regularization Selection (StARS) is implemented to drive the selection of the regularization parameter. The method is fully described in <doi:10.1093/bioinformatics/btx819>.

Computes RGB-based vegetation, color, and spectral indices from digital images for applications in agriculture, crop phenotyping, and remote sensing. The methods are based on digital image processing and plant phenotyping approaches (Singh et al. (2023) <doi:10.1080/10106049.2022.2160831>).

The ecocrop model estimates environmental suitability for plants using a limiting factor approach for plant growth following Hackett (1991) <doi:10.1007/BF00045728>. The implementation in this package is fast and flexible: it allows for the use of any (environmental) predictor variable. Predictors can be either static (for example, soil pH) or dynamic (for example, monthly precipitation).

Processes and visualizes the output of complex phylogenetic analyses from the RevBayes phylogenetic graphical modeling software.

Calculate RNNI distance between and manipulate with ranked trees. RNNI stands for Ranked Nearest Neighbour Interchange and is an extension of the classical NNI space (space of trees created by the NNI moves) to ranked trees, where internal nodes are ordered according to their heights (usually assumed to be times). The RNNI distance takes the tree topology into account, as standard NNI does, but also penalizes changes in the order of internal nodes, i.e. changes in the order of times of evolutionary events. For more information about the RNNI space see: Gavryushkin et al. (2018) <doi:10.1007/s00285-017-1167-9>, Collienne & Gavryushkin (2021) <doi:10.1007/s00285-021-01567-5>, Collienne et al. (2021) <doi:10.1007/s00285-021-01685-0>, and Collienne (2021) <http://hdl.handle.net/10523/12606>.

Enhances the R Optimization Infrastructure ('ROI') package with the quadratic solver qpOASES'. More information about qpOASES can be found at <https://github.com/coin-or/qpOASES>.

This package provides functions for reading data sets in different formats for testing machine learning tools are provided. This allows to run a loop over several data sets in their original form, for example if they are downloaded from UCI Machine Learning Repository. The data are not part of the package and have to be downloaded separately.

Streamlines data preprocessing, analysis, and visualization for association rule mining. Designed to work with the arules package, features include discretizing data frames, generating rule set intersections, and visualizing rules with heatmaps and Euler diagrams. RulesTools also includes a dataset on Brook trout detection from Nolan et al. (2022) <doi:10.1007/s13412-022-00800-x>.

Allows users to import data files containing heartbeat positions in the most broadly used formats, to remove outliers or points with unacceptable physiological values present in the time series, to plot HRV data, and to perform time domain, frequency domain and nonlinear HRV analysis. See Garcia et al. (2017) <DOI:10.1007/978-3-319-65355-6>.

An implementation of the WOFOST ("World Food Studies") crop growth model. WOFOST is a dynamic simulation model that uses daily weather data, and crop, soil and management parameters to simulate crop growth and development. See De Wit et al. (2019) <doi:10.1016/j.agsy.2018.06.018> for a recent review of the history and use of the model.

This package implements the pseudo-R2D2 prior for ordinal regression from the paper "Pseudo-R2D2 prior for high-dimensional ordinal regression" by Yanchenko (2025) <doi:10.1007/s11222-025-10667-x>. In particular, it provides code to evaluate the probability distribution function for the cut-points, compute the log-likelihood, calculate the hyper-parameters for the global variance parameter, find the distribution of McFadden's coefficient-of-determination, and fit the model in rstan'. Please cite the paper if you use these codes.

This package provides functions allowing the user to recursively extract frequent patterns and confident rules according to indicators of minimal support and minimal confidence. These functions are described in "Recursive Association Rule Mining" Abdelkader Mokkadem, Mariane Pelletier, Louis Raimbault (2020) <arXiv:2011.14195>.

Create presentations and display them inside the R REPL (Read-Eval-Print loop), aka the R console. Presentations can be written in RMarkdown or any other text format. A set of convenient navigation options as well as code evaluation during a presentation is provided. It is great for tech talks with live coding examples and tutorials. While this is not a replacement for standard presentation formats, it's old-school looks might just be what sets it apart. This project has been inspired by the REPLesent project for presentations in the Scala REPL'.

Providing the container for the DockerParallel package.

The rerddapUtils package is an R package that is a set of four main functions designed to work with and extend the rerddap package. These functions includes one for restricting by season, one for splitting large requests, and two for working with projected datasets. There are also two utility functions that provide estimates of the size of a proposed rerddap::griddap() request.

This package provides a tool for processing Articulate Assistant Advancedâ ¢ (AAA) ultrasound tongue imaging data and Carstens AG500/1 electro-magnetic articulographic data.

Three methods to calculate R2 for models with correlated errors, including Phylogenetic GLS, Phylogenetic Logistic Regression, Linear Mixed Models (LMMs), and Generalized Linear Mixed Models (GLMMs). See details in Ives 2018 <doi:10.1093/sysbio/syy060>.