Implementation of a procedure---Domingue (2012) <https://eric.ed.gov/?id=ED548657>, Domingue (2014) <doi:10.1007/s11336-013-9342-4>; see also Karabatsos (2001) <https://psycnet.apa.org/record/2002-01665-005> and Kyngdon (2011) <doi:10.1348/2044-8317.002004>---to test the single and double cancellation axioms of conjoint measure in data that is dichotomously coded and measured with error.

This is the core package that provides both the user API and developer API to deploy the parallel cluster on the cloud using the container service. The user can call clusterPreset() to define the cloud service provider and container and makeDockerCluster() to create the cluster. The developer should see "developer's cookbook" on how to define the cloud provider and container.

Defines a collection of functions to compute average power and sample size for studies that use the false discovery rate as the final measure of statistical significance. A three-rectangle approximation method of a p-value histogram is proposed to derive a formula to compute the statistical power for analyses that involve the FDR. The methodology paper of this package is under review.

Creating a great user interface for your Shiny apps can be a hassle, especially if you want to work purely in R and don't want to use, for instance HTML templates. This package adds support for a powerful UI library Fomantic UI - <https://fomantic-ui.com/> (before Semantic). It also supports universal UI input binding that works with various DOM elements.

Draw syntenic relationships between genome assemblies. There are 3 functions which take a tab delimited file containing alignment data for syntenic blocks between genomes to produce either a linear alignment plot, an evolution highway style plot, or a painted ideogram representing syntenic relationships. There is also a function to convert alignment data in the DESCHRAMBLER/inferCAR format to the required data structure.

This package provides deep learning models for time series forecasting using Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). These models capture temporal dependencies and address vanishing gradient issues in sequential data. The package enables efficient forecasting for univariate time series. For methodological details see Jaiswal and co-authors (2022). <doi:10.1007/s00521-021-06621-3>.

This package provides a robust and reproducible pipeline for extracting, cleaning, and analyzing athlete performance data generated by VALD ForceDecks systems. The package supports batch-oriented data processing for large datasets, standardized data transformation workflows, and visualization utilities for sports science research and performance monitoring. It is designed to facilitate reproducible analysis across multiple sports with comprehensive documentation and error handling.

romanisim is a Galsim-based simulator of imaging data from the WFI on the Nancy Grace Roman Space Telescope (pronounced roman-eye-sim, stylized Roman I-Sim). It uses Galsim to render astronomical scenes, WebbPSF to model the point spread function, and CRDS to access the calibration information needed to produce realistic WFI images.

Stores expression profiling data from experiments compatible with the multiWGCNA R package. This includes human postmortem microarray data from patients and controls (GSE28521), astrocyte Ribotag RNA-seq data from EAE and wildtype mice (GSE100329), and mouse RNA-seq data from tau pathology (rTg4510) and wildtype control mice (GSE125957). These data can be accessed using the ExperimentHub workflow (see multiWGCNA vignettes).

Collection of Visium spatial gene expression datasets by 10X Genomics, formatted into objects of class SpatialExperiment. Data cover various organisms and tissues, and include: single- and multi-section experiments, as well as single sections subjected to both whole transcriptome and targeted panel analysis. Datasets may be used for testing of and as examples in packages, for tutorials and workflow demonstrations, or similar purposes.

Tool to find where a function has its lowest value(minimum). The functions can be any dimensions. Recommended use is with eps=10^-10, but can be run with 10^-20, although this depends on the function. Two more methods are in this package, simple gradient method (Gradmod) and Powell method (Powell). These are not recommended for use, their purpose are purely for comparison.

Cases are matched to controls in an efficient, optimal and computationally flexible way. It uses the idea of sub-sampling in the level of the case, by creating pseudo-observations of controls. The user can select between replacement and without replacement, the number of controls, and several covariates to match upon. See Mamouris (2021) <doi:10.1186/s12874-021-01256-3> for an overview.

Implement Bayesian multilevel modelling for compositional data. Compute multilevel compositional data and perform log-ratio transforms at between and within-person levels, fit Bayesian multilevel models for compositional predictors and outcomes, and run post-hoc analyses such as isotemporal substitution models. References: Le, Stanford, Dumuid, and Wiley (2025) <doi:10.1037/met0000750>, Le, Dumuid, Stanford, and Wiley (2025) <doi:10.1080/00273171.2025.2565598>.

Headless companion to the Venn Diagram Lab web tool (<https://www.venndiagramlab.org/>). Build, render, and statistically analyze Venn / UpSet diagrams from CSV / TSV / GMT / GMX inputs. Provides the same 44 SVG models, intersection / Jaccard / hypergeometric statistics, and PDF report layout as the web tool, with byte-equivalent TSV exports (parity-tested against the published Python package). Integrates with ggplot2', tidygraph', and broom'.

Facilitates querying data from the â Facebook Marketing API', particularly for social science research <https://developers.facebook.com/docs/marketing-apis/>. Data from the Facebook Marketing API has been used for a variety of social science applications, such as for poverty estimation (Marty and Duhaut (2024) <doi:10.1038/s41598-023-49564-6>), disease surveillance (Araujo et al. (2017) <doi:10.48550/arXiv.1705.04045>), and measuring migration (Alexander, Polimis, and Zagheni (2020) <doi:10.1007/s11113-020-09599-3>). The package facilitates querying the number of Facebook daily/monthly active users for multiple location types (e.g., from around a specific coordinate to an administrative region) and for a number of attribute types (e.g., interests, behaviors, education level, etc). The package supports making complex queries within one API call and making multiple API calls across different locations and/or parameters.

Ceteris Paribus Profiles (What-If Plots) are designed to present model responses around selected points in a feature space. For example around a single prediction for an interesting observation. Plots are designed to work in a model-agnostic fashion, they are working for any predictive Machine Learning model and allow for model comparisons. Ceteris Paribus Plots supplement the Break Down Plots from breakDown package.

This package provides functions of five estimation method for ED50 (50 percent effective dose) are provided, and they are respectively Dixon-Mood method (1948) <doi:10.2307/2280071>, Choi's original turning point method (1990) <doi:10.2307/2531453> and it's modified version given by us, as well as logistic regression and isotonic regression. Besides, the package also supports comparison between two estimation results.

Designed to facilitate the preprocessing and linking of GIS (Geographic Information System) databases <https://www.sciencedirect.com/topics/computer-science/gis-database>, the R package GISINTEGRATION offers a robust solution for efficiently preparing GIS data for advanced spatial analyses. This package excels in simplifying intrica procedures like data cleaning, normalization, and format conversion, ensuring that the data are optimally primed for precise and thorough analysis.

The Open Data Format (ODF) is a new, non-proprietary, multilingual, metadata enriched, and zip-compressed data format with metadata structured in the Data Documentation Initiative (DDI) Codebook standard. This package allows reading and writing of data files in the Open Data Format (ODF) in R, and displaying metadata in different languages. For further information on the Open Data Format, see <https://opendataformat.github.io/>.

Computes optimal changepoint models using the Poisson likelihood for non-negative count data, subject to the PeakSeg constraint: the first change must be up, second change down, third change up, etc. For more info about the models and algorithms, read "Constrained Dynamic Programming and Supervised Penalty Learning Algorithms for Peak Detection" <https://jmlr.org/papers/v21/18-843.html> by TD Hocking et al.

This package provides a collection of functions for estimating spatial regimes, aggregations of neighboring spatial units that are homogeneous in functional terms. The term spatial regime, therefore, should not be understood as a synonym for cluster. More precisely, the term cluster does not presuppose any functional relationship between the variables considered, while the term regime is linked to a regressive relationship underlying the spatial process.

This package provides tools for evaluating the trustworthiness of machine learning models in production and research settings. Computes a Stability Index that quantifies the consistency of model predictions across multiple runs or resamples, and a Robustness Score that measures model resilience under small input perturbations. Designed for data scientists, ML engineers, and researchers who need to monitor and ensure model reliability, reproducibility, and deployment readiness.

This package allows importing most common specific structure (motif) types into R for use by functions provided by other Bioconductor motif-related packages. Motifs can be exported into most major motif formats from various classes as defined by other Bioconductor packages. A suite of motif and sequence manipulation and analysis functions are included, including enrichment, comparison, P-value calculation, shuffling, trimming, higher-order motifs, and others.

This package provides datasets for the nullranges package vignette, in particular example datasets for DNase hypersensitivity sites (DHS), CTCF binding sites, and CTCF genomic interactions. These are used to demonstrate generation of null hypothesis feature sets, either through block bootstrapping or matching, in the nullranges vignette. For more details, see the data object man pages, and the R scripts for object construction provided within the package.