Simple result caching in R based on R.cache. The global environment is not considered when caching results simplifying moving files between multiple instances of R. Relies on more base functions than R.cache (e.g. cached results are saved using saveRDS() and readRDS()).

Store persistent and synchronized data from shiny inputs within the browser. Refresh shiny applications and preserve user-inputs over multiple sessions. A database-like storage format is implemented using Dexie.js <https://dexie.org>, a minimal wrapper for IndexedDB'. Transfer browser link parameters to shiny input or output values. Store app visitor views, likes and followers.

Infrastructure and functions that can be used for integrating Stan (Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>) code into stand alone R packages which in turn use the CmdStan engine which is often accessed through CmdStanR'. Details given in Stan Development Team (2025) <https://mc-stan.org/cmdstanr/>. Using CmdStanR and pre-written Stan code can make package installation easy. Using staninside offers a way to cache user-compiled Stan models in user-specified directories reducing the need to recompile the same model multiple times.

This package provides Markov Chain Monte Carlo (MCMC) routine for the structural equation modelling described in Maity et. al. (2020) <doi:10.1093/bioinformatics/btaa286>. This MCMC sampler is useful when one attempts to perform an integrative survival analysis for multiple platforms of the Omics data where the response is time to event and the predictors are different omics expressions for different platforms.

The predictive value of a statistical model can often be improved by applying shrinkage methods. This can be achieved, e.g., by regularized regression or empirical Bayes approaches. Various types of shrinkage factors can also be estimated after a maximum likelihood. While global shrinkage modifies all regression coefficients by the same factor, parameterwise shrinkage factors differ between regression coefficients. With variables which are either highly correlated or associated with regard to contents, such as several columns of a design matrix describing a nonlinear effect, parameterwise shrinkage factors are not interpretable and a compromise between global and parameterwise shrinkage, termed joint shrinkage', is a useful extension. A computational shortcut to resampling-based shrinkage factor estimation based on DFBETA residuals can be applied. Global, parameterwise and joint shrinkage for models fitted by lm(), glm(), coxph(), or mfp() is available.

This package provides functions to estimate, predict and interpolate areal data. For estimation and prediction we assume areal data is an average of an underlying continuous spatial process as in Moraga et al. (2017) <doi:10.1016/j.spasta.2017.04.006>, Johnson et al. (2020) <doi:10.1186/s12942-020-00200-w>, and Wilson and Wakefield (2020) <doi:10.1093/biostatistics/kxy041>. The interpolation methodology is (mostly) based on Goodchild and Lam (1980, ISSN:01652273).

Taxonomic dictionaries, formative element lists, and functions related to the maintenance, development and application of U.S. Soil Taxonomy. Data and functionality are based on official U.S. Department of Agriculture sources including the latest edition of the Keys to Soil Taxonomy. Descriptions and metadata are obtained from the National Soil Information System or Soil Survey Geographic databases. Other sources are referenced in the data documentation. Provides tools for understanding and interacting with concepts in the U.S. Soil Taxonomic System. Most of the current utilities are for working with taxonomic concepts at the "higher" taxonomic levels: Order, Suborder, Great Group, and Subgroup.

Enables the ability to change or flash the title of the browser window during a shiny session.

An implementation of statistical tools for the analysis of rotation-valued time series and functional data. It relies on pre-existing quaternion data structure provided by the Eigen C++ library.

This htmlwidget provides pan and zoom interactivity to R graphics, including base', lattice', and ggplot2'. The interactivity is provided through the svg-pan-zoom.js library. Various options to the widget can tailor the pan and zoom experience to nearly any user desire.

Estimation of the required sample size to validate a risk model for binary outcomes, based on the sample size equations proposed by Pavlou et al. (2021) <doi:10.1177/09622802211007522>. For precision-based sample size calculations, the user is required to enter the anticipated values of the C-statistic and outcome prevalence, which can be obtained from a previous study. The user also needs to specify the required precision (standard error) for the C-statistic, the calibration slope and the calibration in the large. The calculations are valid under the assumption of marginal normality for the distribution of the linear predictor.

This package provides a function for the estimation of parameters in a binary regression with the skew-probit link function. Naive MLE, Jeffrey type of prior and Cauchy prior type of penalization are implemented, as described in DongHyuk Lee and Samiran Sinha (2019+) <doi:10.1080/00949655.2019.1590579>.

Created for population health analytics and monitoring. The functions in this package work best when working with patient level Master Patient Index-like datasets . Built to be used by NHS bodies and other health service providers.

This package provides a robust solution employing the SRS (Simple Random Sampling), systematic and PPS (Probability Proportional to Size) sampling methods, ensuring a methodical and representative selection of data. Seamlessly allocate predetermined allocations to smaller levels.

Fits, spatially predicts and temporally forecasts large amounts of space-time data using [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio-temporal big-n problems. Bakar and Sahu (2015) <doi:10.18637/jss.v063.i15>.

This package provides tools for using the StreamCat and LakeCat API and interacting with the StreamCat and LakeCat database. Convenience functions in the package wrap the API for StreamCat on <https://api.epa.gov/StreamCat/streams/metrics>.

Transformation of sea currents to connectivity data. Two files of horizontal and vertical currents flows are transformed into connectivity data in the form of sfnetwork', shapefile, edge list and adjacency matrix. An application example is shown at Nagkoulis et al. (2025) <doi:10.1016/j.dib.2024.111268>.

Combines information from two independent surveys using a model-assisted projection method. Designed for survey sampling scenarios where a large sample collects only auxiliary information (Survey 1) and a smaller sample provides data on both variables of interest and auxiliary variables (Survey 2). Implements a working model to generate synthetic values of the variable of interest by fitting the model to Survey 2 data and predicting values for Survey 1 based on its auxiliary variables (Kim & Rao, 2012) <doi:10.1093/biomet/asr063>.

This package implements a parameter expanded variational Bayes algorithm for linear regression models with high-dimensional variable selection. The methodology utilizes spike-and-slab priors to perform simultaneous estimation and selection. Details can be found in Olejua et al. (2024) <doi:10.21203/rs.3.rs-7208847/v1>.

Statistical Methods for Inferring Transmissions of Infectious Diseases from deep sequencing data (SMITID). It allow sequence-space-time host and viral population data storage, indexation and querying.

This package provides tools to help tag and validate data according to user-specified rules. The safeframe class adds variable level attributes to data.frame columns. Once tagged, these variables can be seamlessly used in downstream analyses, making data pipelines clearer, more robust, and more reliable.

Conducts hierarchical partitioning to calculate individual contributions of spatial and predictors (groups) towards total R2 for spatial simultaneous autoregressive model.

This package provides a process-oriented and trajectory-based Discrete-Event Simulation (DES) package for R. It is designed as a generic yet powerful framework. The architecture encloses a robust and fast simulation core written in C++ with automatic monitoring capabilities. It provides a rich and flexible R API that revolves around the concept of trajectory, a common path in the simulation model for entities of the same type. Documentation about simmer is provided by several vignettes included in this package, via the paper by Ucar, Smeets & Azcorra (2019, <doi:10.18637/jss.v090.i02>), and the paper by Ucar, Hernández, Serrano & Azcorra (2018, <doi:10.1109/MCOM.2018.1700960>); see citation("simmer") for details.

Interfaces the stepcount Python module <https://github.com/OxWearables/stepcount> to estimate step counts and other activities from accelerometry data.