This package performs an analysis of time-to-event clinical trial data using various "win time" methods, including ewt', ewtr', rmt', ewtp', rewtp', ewtpr', rewtpr', max', wtr', rwtr', pwt', and rpwt'. These methods are used to calculate and compare treatment effects on ordered composite endpoints. The package handles event times, event indicators, and treatment arm indicators and supports calculations on observed and resampled data. Detailed explanations of each method and usage examples are provided in "Use of win time for ordered composite endpoints in clinical trials," by Troendle et al. (2024)<doi:10.1002/sim.10045>. For more information, see the package documentation or the vignette titled "Introduction to wintime.".

Easily plot heat maps of the world, based on continuous or categorical data. Country labels can also be added to the map.

This package provides a convenient data set, a set of helper functions, and a benchmark function for economically (profit) driven wind farm layout optimization. This enables researchers in the field of the NP-hard (non-deterministic polynomial-time hard) problem of wind farm layout optimization to focus on their optimization methodology contribution and also provides a realistic benchmark setting for comparability among contributions. See Croonenbroeck, Carsten & Hennecke, David (2020) <doi:10.1016/j.energy.2020.119244>.

This package provides a computationally efficient way of fitting weighted linear fixed effects estimators for causal inference with various weighting schemes. Weighted linear fixed effects estimators can be used to estimate the average treatment effects under different identification strategies. This includes stratified randomized experiments, matching and stratification for observational studies, first differencing, and difference-in-differences. The package implements methods described in Imai and Kim (2017) "When should We Use Linear Fixed Effects Regression Models for Causal Inference with Longitudinal Data?", available at <https://imai.fas.harvard.edu/research/FEmatch.html>.

The shiny application Wallace is a modular platform for reproducible modeling of species niches and distributions. Wallace guides users through a complete analysis, from the acquisition of species occurrence and environmental data to visualizing model predictions on an interactive map, thus bundling complex workflows into a single, streamlined interface. An extensive vignette, which guides users through most package functionality can be found on the package's GitHub Pages website: <https://wallaceecomod.github.io/wallace/articles/tutorial-v2.html>.

R binding for libfswatch', a file system monitoring library. Watch files, or directories recursively, for changes in the background. Log activity, or call an R function, upon every change event.

The web version WebGestalt <https://www.webgestalt.org> supports 12 organisms, 354 gene identifiers and 321,251 function categories. Users can upload the data and functional categories with their own gene identifiers. In addition to the Over-Representation Analysis, WebGestalt also supports Gene Set Enrichment Analysis and Network Topology Analysis. The user-friendly output report allows interactive and efficient exploration of enrichment results. The WebGestaltR package not only supports all above functions but also can be integrated into other pipeline or simultaneously analyze multiple gene lists.

This package provides tools for fitting and simulating mixtures of Watson distributions. The package is described in Sablica, Hornik and Leydold (2026) <doi:10.18637/jss.v115.i04>. The random sampling scheme of the package offers two sampling algorithms that are based of the results of Sablica, Hornik and Leydold (2022) <doi:10.1080/10618600.2024.2416521>. What is more, the package offers a smart tool to combine these two methods, and based on the selected parameters, it approximates the relative sampling speed for both methods and picks the faster one. In addition, the package offers a fitting function for the mixtures of Watson distribution, that uses the expectation-maximization (EM) algorithm. Special features are the possibility to use multiple variants of the E-step and M-step, sparse matrices for the data representation and state of the art methods for numerical evaluation of needed special functions using the results of Sablica and Hornik (2022) <doi:10.1090/mcom/3690> and Sablica and Hornik (2024) <doi:10.1016/j.jmaa.2024.128262>.

Estimation of Panel Quantile Autoregressive Distributed Lag (PQARDL) models that combine panel ARDL methodology with quantile regression. Supports Pooled Mean Group (PMG), Mean Group (MG), and Dynamic Fixed Effects (DFE) estimators across multiple quantiles. Computes long-run cointegrating parameters, error correction term speed of adjustment, half-life of adjustment, and performs Wald tests for parameter equality across quantiles. Based on the econometric frameworks of Pesaran, Shin, and Smith (1999) <doi:10.1080/01621459.1999.10474156>, Cho, Kim, and Shin (2015) <doi:10.1016/j.jeconom.2015.05.003>, and Bildirici and Kayikci (2022).

This package implements the Cross-Sectionally Augmented Panel Quantile Autoregressive Distributed Lag (CS-PQARDL) model and the Quantile Common Correlated Effects Mean Group (QCCEMG) estimator for panel data with cross-sectional dependence. The package handles unobserved common factors through cross-sectional averages following Pesaran (2006) <doi:10.1111/j.1468-0262.2006.00692.x> and Chudik and Pesaran (2015) <doi:10.1016/j.jeconom.2015.03.007>. Quantile regression for dynamic panels follows Harding, Lamarche, and Pesaran (2020) <doi:10.1002/jae.2753>. The ARDL approach to cointegration testing is based on Pesaran, Shin, and Smith (2001) <doi:10.1002/jae.616>.

Create HTML5 slides with R Markdown and the JavaScript library remark.js (<https://remarkjs.com>).

The XKCD color survey asked participants to name colours. Randall Munroe published the top thousand(roughly) names and their sRGB hex values. This package lets you use them.

This package implements panel cointegration tests allowing for structural breaks and cross-section dependence following the methodology of Banerjee and Carrion-i-Silvestre (2015) <doi:10.1002/jae.2348>. The package provides iterative factor-break estimation, individual ADF tests on defactored residuals, standardized panel test statistics, and the Bai and Ng (2004) <doi:10.1111/j.1468-0262.2004.00528.x> MQ test for identifying common stochastic trends. Supports five model specifications with varying deterministic components and break structures.

This package provides tools for interactive data exploration built using shiny'. Includes apps for descriptive statistics, visualizing probability distributions, inferential statistics, linear regression, logistic regression and RFM analysis.

This package implements the interactive fixed effects ('IFE') panel estimator of Bai (2009) <doi:10.3982/ECTA6135> for balanced and unbalanced panels, with optional additive unit and/or time fixed effects. Provides analytical standard errors ('homoskedastic', HC1 heteroskedasticity-robust, cluster-robust by unit, and heteroskedasticity- and autocorrelation- consistent), together with asymptotic incidental-parameter bias correction for large panels, including a dynamic extension for predetermined (lagged-dependent) regressors following Moon and Weidner (2017) <doi:10.1017/S0266466615000328>. The number of factors is chosen by information criteria (Bai and Ng 2002 <doi:10.1111/1468-0262.00273>) or by singular value thresholding. Unbalanced panels are handled by an expectation-maximisation algorithm with nuclear-norm-regularised initialisation, with estimation, analytical inference, and bias correction following Su, Wang and Wang (2025) <doi:10.2139/ssrn.5177283> and building on the matrix-completion and missing-data factor analysis of Bai and Ng (2021) <doi:10.1080/01621459.2021.1967163>. All computations use base R only, with no external dependencies.

Read and write XES Files to create event log objects used by the bupaR framework. XES (Extensible Event Stream) is the `IEEE` standard for storing and sharing event data (see <http://standards.ieee.org/findstds/standard/1849-2016.html> for more info).

Converts XML documents to R dataframes and dataframes to XML documents. A wide variety of options allows for different XML formats and flexible control of the conversion process. Results can be exported to CSV and Excel, if desired. Also converts XML data to R lists.

An extension for the xml2 package to transform XML documents by applying an xslt style-sheet.

This package contains functions to identify tree-ring borders based on X-ray micro-density profiles and a Graphical User Interface (GUI) to visualize density profiles and correct tree-ring borders. Campelo F, Mayer K, Grabner M. (2019) <doi:10.1016/j.dendro.2018.11.002>.

Analysis of crossover interference in experimental crosses, particularly regarding the gamma model. See, for example, Broman and Weber (2000) <doi:10.1086/302923>.

This package provides a toolbox for meta-analysis. This package includes: 1,a robust multivariate meta-analysis of continuous or binary outcomes; 2, a bivariate Egger's test for detecting small study effects; 3, Galaxy Plot: A New Visualization Tool of Bivariate Meta-Analysis Studies; 4, a bivariate T&F method accounting for publication bias in bivariate meta-analysis, based on symmetry of the galaxy plot. Hong C. et al(2020) <doi:10.1093/aje/kwz286>, Chongliang L. et al(2020) <doi:10.1101/2020.07.27.20161562>.

Supports a structured approach for exploring PKPD data <https://opensource.nibr.com/xgx/>. It also contains helper functions for enabling the modeler to follow best R practices (by appending the program name, figure name location, and draft status to each plot). In addition, it enables the modeler to follow best graphical practices (by providing a theme that reduces chart ink, and by providing time-scale, log-scale, and reverse-log-transform-scale functions for more readable axes). Finally, it provides some data checking and summarizing functions for rapidly exploring pharmacokinetics and pharmacodynamics (PKPD) datasets.

Draw images easily, set up a plot with an image, specify where that image should be placed. Image plot by default reflects the index of the image data itself, or can be specified in simple extent terms xmin,xmax,ymin,ymax'. Numeric matrices, integer arrays, byte arrays, character arrays, and native rasters are (or will be) supported. A combination of image() and rasterImage() from the graphics package with their good features in one place.

Extras and extensions for xaringan slides. Navigate your slides with tile view. Make your slides editable, live! Announce slide changes with subtle tones. Animate slide transitions with animate.css'. Add tabbed panels to slides with panelset'. Use the Tachyons CSS utility toolkit for rapid slide development. Scribble on your slides. Add a copy button to your code chunks with clipboard'. Add a logo or top or bottom banner to every slide. Broadcast slides to stay in sync with remote viewers. Include yourself in your slides with webcam'. Plus a whole lot more!