The explosion of biobank data offers immediate opportunities for gene-environment (GxE) interaction studies of complex diseases because of the large sample sizes and rich collection in genetic and non-genetic information. However, the extremely large sample size also introduces new computational challenges in GxE assessment, especially for set-based GxE variance component (VC) tests, a widely used strategy to boost overall GxE signals and to evaluate the joint GxE effect of multiple variants from a biologically meaningful unit (e.g., gene). We present SEAGLE', a Scalable Exact AlGorithm for Large-scale Set-based GxE tests, to permit GxE VC test scalable to biobank data. SEAGLE employs modern matrix computations to achieve the same â exactâ results as the original GxE VC tests, and does not impose additional assumptions nor relies on approximations. SEAGLE can easily accommodate sample sizes in the order of 10^5, is implementable on standard laptops, and does not require specialized equipment. The accompanying manuscript for this package can be found at Chi, Ipsen, Hsiao, Lin, Wang, Lee, Lu, and Tzeng. (2021+) <arXiv:2105.03228>.

It is a single cell active pathway analysis tool based on the graph neural network (F. Scarselli (2009) <doi:10.1109/TNN.2008.2005605>; Thomas N. Kipf (2017) <arXiv:1609.02907v4>) to construct the gene-cell association network, infer pathway activity scores from different single cell modalities data, integrate multiple modality data on the same cells into one pathway activity score matrix, identify cell phenotype activated gene modules and parse association networks of gene modules under multiple cell phenotype. In addition, abundant visualization programs are provided to display the results.

Companion package that supports the surveydown survey platform (<https://surveydown.org>). The default method for working with a surveydown survey is to edit the plain text survey.qmd and app.R files. With sdstudio', you can create, preview and manage surveys with a shiny application as a graphical user interface.

Potential randomization schemes are prospectively evaluated when units are assigned to treatment arms upon entry into the experiment. The schemes are evaluated for balance on covariates and on predictability (i.e., how well could a site worker guess the treatment of the next unit enrolled).

Generates cell-level cytokine activity estimates using relevant information from gene sets constructed with the CytoSig and the Reactome databases and scored using the modified Variance-adjusted Mahalanobis (VAM) framework for single-cell RNA-sequencing (scRNA-seq) data. CytoSig database is described in: Jiang at al., (2021) <doi:10.1038/s41592-021-01274-5>. Reactome database is described in: Gillespie et al., (2021) <doi:10.1093/nar/gkab1028>. The VAM method is outlined in: Frost (2020) <doi:10.1093/nar/gkaa582>.

This package provides a comprehensive framework for quantifying the fundamental thermodynamic parameters of adsorption reactionsâ changes in the standard Gibbs free energy (delta G), enthalpy (delta H), and entropy (delta S)â is essential for understanding the spontaneity, heat effects, and molecular ordering associated with sorption processes. By analysing temperature-dependent equilibrium data, thermodynamic interpretation expands adsorption studies beyond conventional isotherm fitting, offering deeper insight into underlying mechanisms and surfaceâ solute interactions. Such an approach typically involves evaluating equilibrium coefficients across multiple temperatures and non-temperature treatments, deriving thermodynamic parameters using established thermodynamic relationships, and determining delta G as a temperature-specific indicator of adsorption favourability. This analytical pathway is widely applicable across environmental science, soil science, chemistry, materials science, and engineering, where reliable assessment of sorption behaviour is critical for examining contaminant retention, nutrient dynamics, and the behaviour of natural and engineered surfaces. By focusing specifically on thermodynamic inference, this framework complements existing adsorption isotherm-fitting packages such as âAdIsMFâ <https://CRAN.R-project.org/package=AdIsMF> <doi:10.32614/CRAN.package.AdIsMF>, and strengthens the scientific basis for interpreting adsorption energetics in both research and applied contexts. Details can be found in Roy et al. (2025) <doi:10.1007/s11270-025-07963-7>.

An interactive charting library built on Svelte and D3 to easily produce SVG charts in R. Designed to simplify shiny development by eliminating the need for renderUI(), insertUI(), removeUI(), and shiny proxy functions, using Svelte''s reactive state system instead.

This package implements named semaphores from the boost C++ library <https://www.boost.org/> for interprocess communication. Multiple R sessions on the same host can block (with optional timeout) on a semaphore until it becomes positive, then atomically decrement it and unblock. Any session can increment the semaphore.

Easily create pretty popup messages (modals) in Shiny'. A modal can contain text, images, OK/Cancel buttons, an input to get a response from the user, and many more customizable options.

This package provides a consistent interface to encrypt and decrypt strings, R objects and files using symmetric and asymmetric key encryption.

Quasi-Monte-Carlo algorithm for systematic generation of shock scenarios from an arbitrary multivariate elliptical distribution. The algorithm selects a systematic mesh of arbitrary fineness that approximately evenly covers an isoprobability ellipsoid in d dimensions (Flood, Mark D. & Korenko, George G. (2013) <doi:10.1080/14697688.2014.926018>). This package is the R analogy to the Matlab code published by Flood & Korenko in above-mentioned paper.

This package provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses, see Budeus (2016) <doi:10.1371/journal.pone.0146409>.

This package provides functionality for working with tensors, alternating forms, wedge products, Stokes's theorem, and related concepts from the exterior calculus. Uses disordR discipline (Hankin, 2022, <doi:10.48550/arXiv.2210.03856>). The canonical reference would be M. Spivak (1965, ISBN:0-8053-9021-9) "Calculus on Manifolds". To cite the package in publications please use Hankin (2022) <doi:10.48550/arXiv.2210.17008>.

This package contains functions for statistical data analysis based on spatially-clustered techniques. The package allows estimating the spatially-clustered spatial regression models presented in Cerqueti, Maranzano \& Mattera (2024), "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe", arXiv preprint 2407.15874 <doi:10.48550/arXiv.2407.15874>. Specifically, the current release allows the estimation of the spatially-clustered linear regression model (SCLM), the spatially-clustered spatial autoregressive model (SCSAR), the spatially-clustered spatial Durbin model (SCSEM), and the spatially-clustered linear regression model with spatially-lagged exogenous covariates (SCSLX). From release 0.0.2, the library contains functions to estimate spatial clustering based on Adiajacent Matrix K-Means (AMKM) as described in Zhou, Liu \& Zhu (2019), "Weighted adjacent matrix for K-means clustering", Multimedia Tools and Applications, 78 (23) <doi:10.1007/s11042-019-08009-x>.

This package provides a matrix-like class to represent a symmetric matrix partitioned into file-backed blocks.

Track and record the use of applications and the user's interactions with Shiny inputs. Allows to trace the inputs with which the user interacts, the outputs generated, as well as the errors displayed in the interface.

This package provides functions for making particle-size analysis. Sieve tests are widely used to obtain particle-size distribution of powders or granular materials.

This package implements the smooth LASSO estimator for the function-on-function linear regression model described in Centofanti et al. (2022) <doi:10.1016/j.csda.2022.107556>.

Hierarchical multistate models are considered to perform the analysis of independent/clustered semi-competing risks data. The package allows to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions and cluster-specific random effects distribution; a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation approach for several parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools.

This package provides predictive accuracy tools to evaluate time-to-event survival models. This includes calculating the concordance probability estimate that incorporates the follow-up time for a particular study developed by Devlin, Gonen, Heller (2020)<doi:10.1007/s10985-020-09503-3>. It also evaluates the concordance probability estimate for nested Cox proportional hazards models using a projection-based approach by Heller and Devlin (under review).

This package implements Bayesian inference in accelerated failure time (AFT) models for right-censored survival times assuming a log-logistic distribution. Details of the variational Bayes algorithms, with and without shared frailty, are described in Xian et al. (2024) <doi:10.1007/s11222-023-10365-6> and Xian et al. (2024) <doi:10.48550/arXiv.2408.00177>, respectively.

This package provides three basic functions that support an implementation of Case 2 (profile case) best-worst scaling. The first is to convert an orthogonal main-effect design into questions, the second is to create a dataset suitable for analysis, and the third is to calculate count-based scores. For details, see Aizaki and Fogarty (2019) <doi:10.1016/j.jocm.2019.100171>.

The SAVVY (Survival Analysis for AdVerse Events with VarYing Follow-Up Times) project is a consortium of academic and pharmaceutical industry partners that aims to improve the analyses of adverse event (AE) data in clinical trials through the use of survival techniques appropriately dealing with varying follow-up times and competing events, see Stegherr, Schmoor, Beyersmann, et al. (2021) <doi:10.1186/s13063-021-05354-x>. Although statistical methodologies have advanced, in AE analyses often the incidence proportion, the incidence density or a non-parametric Kaplan-Meier estimator are used, which either ignore censoring or competing events. This package contains functions to easily conduct the proposed improved AE analyses.

Takea Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA) for polytomous items. Package includes functions for generating a sequential relation table and a treegram to visualize the sequential relations between pairs of items.