Create Upset plots using a combination of ggplot2 and patchwork'.

Utility functions that help with common base-R problems relating to lists. Lists in base-R are very flexible. This package provides functions to quickly and easily characterize types of lists. That is, to identify if all elements in a list are null, data.frames, lists, or fully named lists. Other functionality is provided for the handling of lists, such as the easy splitting of lists into equally sized groups, and the unnesting of data.frames within fully named lists.

Displays the content of a R script into the Cytoscape network-visualization app <https://cytoscape.org/>.

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>.

Utilities for single nucleotide polymorphism (SNP) based kinship analysis testing and evaluation. The skater package contains functions for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing identity by descent (IBD) segment data. Package functions and methods are described in Turner et al. (2021) "skater: An R package for SNP-based Kinship Analysis, Testing, and Evaluation" <doi:10.1101/2021.07.21.453083>.

Fits single-species (univariate) and multi-species (multivariate) non-spatial and spatial abundance models in a Bayesian framework using Markov Chain Monte Carlo (MCMC). Spatial models are fit using Nearest Neighbor Gaussian Processes (NNGPs). Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and Banerjee (2022) <doi:10.18637/jss.v103.i05>. Fits single-species and multi-species spatial and non-spatial versions of generalized linear mixed models (Gaussian, Poisson, Negative Binomial), N-mixture models (Royle 2004 <doi:10.1111/j.0006-341X.2004.00142.x>) and hierarchical distance sampling models (Royle, Dawson, Bates (2004) <doi:10.1890/03-3127>). Multi-species spatial models are fit using a spatial factor modeling approach with NNGPs for computational efficiency.

This package provides a fast implementation of the weighted information similarity aggregation (WISE) test for detecting serial dependence, particularly suited for high-dimensional and non-Euclidean time series. Includes functions for constructing similarity matrices and conducting hypothesis testing. Users can use different similarity measures and define their own weighting schemes. For more details see Q Zhu, M Liu, Y Han, D Zhou (2025) <doi:10.48550/arXiv.2509.05678>.

Settings and functions to extend the knitr SAS engine.

Normalization based a subset of negative control probes as described in Subset quantile normalization using negative control features'. Wu Z, Aryee MJ, J Comput Biol. 2010 Oct;17(10):1385-95 [PMID 20976876].

Estimation of various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.

Manage a collection/library of R source packages. Discover, document, load, test source packages. Enable to use those packages as if they were actually installed. Quickly reload only what is needed on source code change. Run tests and checks in parallel.

This package provides tools for performing variable selection in three-way data using N-PLS in combination with L1 penalization, Selectivity Ratio and VIP scores. The N-PLS model (Rasmus Bro, 1996 <DOI:10.1002/(SICI)1099-128X(199601)10:1%3C47::AID-CEM400%3E3.0.CO;2-C>) is the natural extension of PLS (Partial Least Squares) to N-way structures, and tries to maximize the covariance between X and Y data arrays. The package also adds variable selection through L1 penalization, Selectivity Ratio and VIP scores.

Add shiny inputs with one or more inline buttons that grow and shrink with inputs. Also add tool tips to input buttons and styling and messages for input validation.

Spatial forecast verification refers to verifying weather forecasts when the verification set (forecast and observations) is on a spatial field, usually a high-resolution gridded spatial field. Most of the functions here require the forecast and observed fields to be gridded and on the same grid. For a thorough review of most of the methods in this package, please see Gilleland et al. (2009) <doi: 10.1175/2009WAF2222269.1> and for a tutorial on some of the main functions available here, see Gilleland (2022) <doi: 10.5065/4px3-5a05>.

This package provides functions to model and forecast crop yields using a spatial temporal conditional copula approach. The package incorporates extreme weather covariates and Bayesian Structural Time Series models to analyze crop yield dependencies across multiple regions. Includes tools for fitting, simulating, and visualizing results. This method build upon established R packages, including Hofert et al'. (2025) <doi:10.32614/CRAN.package.copula>, Scott (2024) <doi:10.32614/CRAN.package.bsts>, and Stephenson et al'. (2024) <doi:10.32614/CRAN.package.evd>.

Implementation of prediction and inference procedures for Synthetic Control methods using least square, lasso, ridge, or simplex-type constraints. Uncertainty is quantified with prediction intervals as developed in Cattaneo, Feng, and Titiunik (2021) <doi:10.1080/01621459.2021.1979561> for a single treated unit and in Cattaneo, Feng, Palomba, and Titiunik (2025) <doi:10.1162/rest_a_01588> for multiple treated units and staggered adoption. More details about the software implementation can be found in Cattaneo, Feng, Palomba, and Titiunik (2025) <doi:10.18637/jss.v113.i01>.

An end-to-end toolkit for land use and land cover classification using big Earth observation data. Builds satellite image data cubes from cloud collections. Supports visualization methods for images and time series and smoothing filters for dealing with noisy time series. Enables merging of multi-source imagery (SAR, optical, DEM). Includes functions for quality assessment of training samples using self-organized maps and to reduce training samples imbalance. Provides machine learning algorithms including support vector machines, random forests, extreme gradient boosting, multi-layer perceptrons, temporal convolution neural networks, and temporal attention encoders. Performs efficient classification of big Earth observation data cubes and includes functions for post-classification smoothing based on Bayesian inference. Enables best practices for estimating area and assessing accuracy of land change. Includes object-based spatio-temporal segmentation for space-time OBIA. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.

Calculation of solar zenith and azimuth angles.

An interface to the Python package stanza <https://stanfordnlp.github.io/stanza/index.html>. stanza is a Python NLP library for many human languages. It contains support for running various accurate natural language processing tools on 60+ languages.

This package provides a graphical user interface (GUI) for fitting Bayesian regression models using the package brms which in turn relies on Stan (<https://mc-stan.org/>). The shinybrms GUI is a shiny app.

This package provides a framework for extracting semantic motifs around entities in textual data. It implements an entity-centered semantic grammar that distinguishes six classes of motifs: actions of an entity, treatments of an entity, agents acting upon an entity, patients acted upon by an entity, characterizations of an entity, and possessions of an entity. Motifs are identified by applying a set of extraction rules to a parsed text object that includes part-of-speech tags and dependency annotations - such as those generated by spacyr'. For further reference, see: Stuhler (2022) <doi: 10.1177/00491241221099551>.

This package provides a set of functions for computing potential evapotranspiration and several widely used drought indices including the Standardized Precipitation-Evapotranspiration Index (SPEI).

SCEPtER pipeline for estimating the stellar age for double-lined detached binary systems. The observational constraints adopted in the recovery are the effective temperature, the metallicity [Fe/H], the mass, and the radius of the two stars. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models.

Simple Component Analysis (SCA) often provides much more interpretable components than Principal Components (PCA) while still representing much of the variability in the data.