Translate differential transcript usage results into discrete splice events.

Utility functions for spectroscopy. 1. Functions to simulate spectra for use in teaching or testing. 2. Functions to process files created by LoggerPro and SpectraSuite software.

Computes sparse vector autoregressive coefficients and sparse precision matrices for time series chain graphical models. Methods are described in Abegaz and Wit (2013) <doi:10.1093/biostatistics/kxt005>.

An R data package containing setlists from all Bruce Springsteen concerts over 1973-2021. Also includes all his song details such as lyrics and albums. Data extracted from: <http://brucebase.wikidot.com/>.

Inspect interactively the spatially-resolved transcriptomics data from the 10x Genomics Visium platform as well as data from the Maynard, Collado-Torres et al, Nature Neuroscience, 2021 project analyzed by Lieber Institute for Brain Development (LIBD) researchers and collaborators.

Visualization and analysis of Vectra Immunoflourescent data. Options for calculating both the univariate and bivariate Ripley's K are included. Calculations are performed using a permutation-based approach presented by Wilson et al. <doi:10.1101/2021.04.27.21256104>.

This package provides functions for differential gene expression analysis of gene expression time-course data. Natural cubic spline regression models are used. Identified genes may further be used for pathway enrichment analysis and/or the reconstruction of time dependent gene regulatory association networks.

This package provides functions to generate or sample from all possible splits of features or variables into a number of specified groups. Also computes the best split selection estimator (for low-dimensional data) as defined in Christidis, Van Aelst and Zamar (2019) <arXiv:1812.05678>.

This package provides sparse vectors powered by ALTREP (Alternative Representations for R Objects) that behave like regular vectors, and can thus be used in data frames. It also provides tools to convert between sparse matrices and data frames with sparse columns and functions to interact with sparse vectors.

Model based clustering with spherical data using mixtures of elliptically symmetric distributions, namely mixtures of spherical elliptically symmetric projected Cauchy (SESPC) or mixtures of elliptically symmetric angular Gaussian (ESAG) distributions. The relevant paper is: Perdikis T., Alharbi N. and Tsagris M. (2026). <doi:10.48550/arXiv.2605.27496>.

Spatially-aware quality control (QC) software for both spot-level and artifact-level QC in spot-based spatial transcripomics, such as 10x Visium. These methods calculate local (nearest-neighbors) mean and variance of standard QC metrics (library size, unique genes, and mitochondrial percentage) to identify outliers spot and large technical artifacts.

Converts hyperspectral reflectance signatures to device- independent sRGB hex codes and CIE L*a*b* coordinates using the CIE 1931 2-degree standard observer and the D65 standard illuminant. Includes bundled reference data so the package is self-contained. Handles missing and non-finite reflectance values via a configurable policy.

Corrects the spelling of a given word in English using a modification of Peter Norvig's spell correct algorithm (see <http://norvig.com/spell-correct.html>) which handles up to three edits. The algorithm tries to find the spelling with maximum probability of intended correction out of all possible candidate corrections from the original word.

This package creates dataset-local persistent identifiers for polygon units observed across time. Configurable overlap metrics, thresholds, and one-to-one matching rules identify continuity while separate identifiers track boundary versions and broader lineages through splits, mergers, and replacements. Candidate-link diagnostics, validation checks, registry reconciliation, and lineage summaries keep the resulting decisions inspectable and reproducible.

Making specification curve analysis easy, fast, and pretty. It improves upon existing offerings with additional features and tidyverse integration. Users can easily visualize and evaluate how their models behave under different specifications with a high degree of customization. For a description and applications of specification curve analysis see Simonsohn, Simmons, and Nelson (2020) <doi:10.1038/s41562-020-0912-z>.

This package performs estimation and testing of the treatment effect in a 2-group randomized clinical trial with a quantitative, dichotomous, or right-censored time-to-event endpoint. The method improves efficiency by leveraging baseline predictors of the endpoint. The inverse probability weighting technique of Robins, Rotnitzky, and Zhao (JASA, 1994) is used to provide unbiased estimation when the endpoint is missing at random.

This package provides the core framework for a discrete event system to implement a complete data-to-decisions, reproducible workflow. The core components facilitate the development of modular pieces, and enable the user to include additional functionality by running user-built modules. Includes conditional scheduling, restart after interruption, packaging of reusable modules, tools for developing arbitrary automated workflows, automated interweaving of modules of different temporal resolution, and tools for visualizing and understanding the within-project dependencies.

SpatialCPie is an R package designed to facilitate cluster evaluation for spatial transcriptomics data by providing intuitive visualizations that display the relationships between clusters in order to guide the user during cluster identification and other downstream applications. The package is built around a shiny "gadget" to allow the exploration of the data with multiple plots in parallel and an interactive UI. The user can easily toggle between different cluster resolutions in order to choose the most appropriate visual cues.

SpectralTAD is an R package designed to identify Topologically Associated Domains (TADs) from Hi-C contact matrices. It uses a modified version of spectral clustering that uses a sliding window to quickly detect TADs. The function works on a range of different formats of contact matrices and returns a bed file of TAD coordinates. The method does not require users to adjust any parameters to work and gives them control over the number of hierarchical levels to be returned.

Selection of spatially balanced samples. In particular, the implemented sampling designs allow to select probability samples well spread over the population of interest, in any dimension and using any distance function (e.g. Euclidean distance, Manhattan distance). For more details, Pantalone F, Benedetti R, and Piersimoni F (2022) <doi:10.18637/jss.v103.c02>, Benedetti R and Piersimoni F (2017) <doi:10.1002/bimj.201600194>, and Benedetti R and Piersimoni F (2017) <arXiv:1710.09116>. The implementation has been done in C++ through the use of Rcpp and RcppArmadillo'.

From output files obtained from the software ModestR', the relative contribution of factors to explain species distribution is depicted using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent.

This package provides computational building blocks for fixed-radius spatial aggregation, weighted circle-placement problems, hotspot detection, and polygon-based spatial summaries. The package focuses on efficient determination of the sum of observations within a given radius, identifying areas of high local concentration, and aggregating point data to polygon geometries. These methods are useful for applications such as insurance, urban analytics, environmental exposure analysis, and other spatial point pattern workflows. The fixed-radius circle placement problem is discussed by Chazelle and Lee (1986) <doi:10.1007/BF02238188>, and related maximum covering problems are described by Church (1974) <doi:10.1007/BF01942293>.

This package provides tools to assess the association between two spatial processes. Currently, several methodologies are implemented: A modified t-test to perform hypothesis testing about the independence between the processes, a suitable nonparametric correlation coefficient, the codispersion coefficient, and an F test for assessing the multiple correlation between one spatial process and several others. Functions for image processing and computing the spatial association between images are also provided. Functions contained in the package are intended to accompany Vallejos, R., Osorio, F., Bevilacqua, M. (2020). Spatial Relationships Between Two Georeferenced Variables: With Applications in R. Springer, Cham <doi:10.1007/978-3-030-56681-4>.

The SparseArray package is an infrastructure package that provides an array-like container for efficient in-memory representation of multidimensional sparse data in R. The package defines the SparseArray virtual class and two concrete subclasses: COO_SparseArray and SVT_SparseArray. Each subclass uses its own internal representation of the nonzero multidimensional data, the "COO layout" and the "SVT layout", respectively. SVT_SparseArray objects mimic as much as possible the behavior of ordinary matrix and array objects in base R. In particular, they support most of the "standard matrix and array API" defined in base R and in the matrixStats package from CRAN.