The Time-Delay Correlation algorithm (TDCor) reconstructs the topology of a gene regulatory network (GRN) from time-series transcriptomic data. The algorithm is described in details in Lavenus et al., Plant Cell, 2015. It was initially developed to infer the topology of the GRN controlling lateral root formation in Arabidopsis thaliana. The time-series transcriptomic dataset which was used in this study is included in the package to illustrate how to use it.

This package provides tools for decomposing differences in rate metrics between two groups into contributions from individual subgroups and visualizing them as a "Theseus Plot". Inspired by the story of the Ship of Theseus, the method replaces subgroup data from one group with that of another step by step, recalculating the overall metric at each stage to quantify subgroup contributions. A Theseus Plot combines the stepwise progression of a waterfall plot with the comparative bars of a bar chart, offering an intuitive way to understand subgroup-level effects.

This package provides a dataset of predefined color palettes based on the Star Trek science fiction series, associated color palette functions, and additional functions for generating customized palettes that are on theme. The package also offers functions for applying the palettes to plots made using the ggplot2 package.

This package implements tic-tac-toe game to play on console, either with human or AI players. Various levels of AI players are trained through the Q-learning algorithm.

Creates, manipulates, queries and repairs vectors of parameter terms. Parameter terms are the labels used to reference values in vectors, matrices and arrays. They represent the names in coefficient tables and the column names in mcmc and mcmc.list objects.

Computes the product moments of the truncated multivariate normal distribution, particularly for cases involving patterned variance-covariance matrices. It also has the capability to calculate these moments with arbitrary positive-definite matrices, although performance may degrade for high-dimensional variables.

This package provides a comprehensive R interface to Python's twscrape library for scraping Twitter/X data. This package uses reticulate to provide a seamless R interface to the fully functional Python twscrape library. Supports searching tweets, user timelines, followers, and more, with built-in rate limiting and multi-account support. Built on top of twscrape by vladkens <https://github.com/vladkens/twscrape> and inspired by snscrape by JustAnotherArchivist <https://github.com/JustAnotherArchivist/snscrape>.

This package provides data sets for teaching statistics and data science courses. It includes a sample of data from John Edmund Kerrich's famous coinflip experiment. These are data that I used for statistics. The package also contains an R Markdown template with the required formatting for assignments in my former courses.

An object model for source text and translations. Find and extract translatable strings. Provide translations and seamlessly retrieve them at runtime.

Analyzes transition trajectories in event, sequence, and ordered data, focusing on how states follow one another, how far processes unfold, and where pathways branch or converge. Trajectories are modeled using variable-order prediction suffix trees (Ron, Singer, & Tishby, 1996) <doi:10.1023/A:1026490906255>, implemented in both frequency-based and prediction-based forms. The framework includes multiple pruning, validation, and smoothing techniques to ensure model robustness. Visualization options include transition trees, radial sunburst diagrams, transition heatmaps, and forward trajectory trees.

This package provides several confidence interval and testing procedures, based on either semiparametric (using event-specific win ratios) or nonparametric measures, including the ratio of integrated cumulative hazard (RICH) and the ratio of integrated transformed cumulative hazard (RITCH), for treatment effect inference with terminal and non-terminal events under competing risks. The semiparametric results were developed in Yang et al. (2022 <doi:10.1002/sim.9266>), and the nonparametric results were developed in Yang (2025 <doi:10.1002/sim.70205>). For comparison, results for the win ratio (Finkelstein and Schoenfeld 1999 <doi:10.1002/(SICI)1097-0258(19990615)18:11%3C1341::AID-SIM129%3E3.0.CO;2-7>), Pocock et al. 2012 <doi:10.1093/eurheartj/ehr352>, and Bebu and Lachin 2016 <doi:10.1093/biostatistics/kxv032>) are included. The package also supports univariate survival analysis with a single event. In this package, effect size estimates and confidence intervals are obtained for each event type, and several testing procedures are implemented for the global null hypothesis of no treatment effect on either terminal or non-terminal events. Furthermore, a test of proportional hazards assumptions, under which the event-specific win ratios converge to hazard ratios, and a test of equal hazard ratios, are provided. For summarizing the treatment effect across all events, confidence intervals for linear combinations of the event-specific win ratios, RICH, or RITCH are available using pre-determined or data-driven weights. Asymptotic properties of these inference procedures are discussed in Yang et al. (2022 <doi:10.1002/sim.9266>) and Yang (2025 <doi:10.1002/sim.70205>).

We propose an optimality criterion to determine the required training set, r-score, which is derived directly from Pearson's correlation between the genomic estimated breeding values and phenotypic values of the test set <doi:10.1007/s00122-019-03387-0>. This package provides two main functions to determine a good training set and its size.

Instance feature calculation and evolutionary instance generation for the traveling salesman problem. Also contains code to "morph" two TSP instances into each other. And the possibility to conveniently run a couple of solvers on TSP instances.

This package provides a standardized user interface for column selection, that facilitates dataset merging in teal framework.

Transport theory has seen much success in many fields of statistics and machine learning. We provide a variety of algorithms to compute Wasserstein distance, barycenter, and others. See Peyré and Cuturi (2019) <doi:10.1561/2200000073> for the general exposition to the study of computational optimal transport.

Extends invariant causal prediction (Peters et al., 2016, <doi:10.1111/rssb.12167>) to generalized linear and transformation models (Hothorn et al., 2018, <doi:10.1111/sjos.12291>). The methodology is described in Kook et al. (2023, <doi:10.1080/01621459.2024.2395588>).

Streamlines the analysis of clinical data by automatically selecting appropriate statistical descriptions and inference methods based on variable types. For method details see Motulsky H J (2016) <https://www.graphpad.com/guides/prism/10/statistics/index.htm> and d'Agostino R B (1971) <doi:10.1093/biomet/58.2.341>.

This package provides a tidy-style interface for applying differential privacy to data frames. Provides pipe-friendly functions to add calibrated noise, compute private statistics, and track privacy budgets using the epsilon-delta differential privacy framework. Implements the Laplace mechanism (Dwork et al. 2006 <doi:10.1007/11681878_14>) and the Gaussian mechanism for achieving differential privacy as described in Dwork and Roth (2014) <doi:10.1561/0400000042>.

This package provides a convenient way to log scalars, images, audio, and histograms in the tfevent record file format. Logged data can be visualized on the fly using TensorBoard', a web based tool that focuses on visualizing the training progress of machine learning models.

This package provides a tidy interface for integrating large language model (LLM) APIs such as Claude', OpenAI', Gemini', Mistral', and local models via Ollama into R workflows. The package supports text, image, audio, video, and document interactions; a unified media interface for attaching inline files or uploading to provider file stores; batch request APIs for cost-efficient large-scale processing; and a pipeline-oriented interface for seamless integration into data workflows. Web services are available at <https://www.anthropic.com>, <https://openai.com>, <https://aistudio.google.com/>, <https://mistral.ai/> and <https://ollama.com>.

This package provides a dependency-light OAuth 2.0 <https://www.rfc-editor.org/rfc/rfc6749> client supporting the client-credentials and authorization-code grants with token refresh. Built on curl and jsonlite', with base R's socket server for the redirect listener, avoiding heavier HTTP stacks.

Converting structured data from tables into XML format using predefined templates ensures consistency and flexibility, making it ideal for data exchange, reporting, and automated workflows.

This package provides functions that provide point and interval estimations of optimum thresholds for continuous diagnostic tests. The methodology used is based on minimizing an overall cost function in the two- and three-state settings. We also provide functions for sample size determination and estimation of diagnostic accuracy measures. We also include graphical tools. The statistical methodology used here can be found in Perez-Jaume et al (2017) <doi:10.18637/jss.v082.i04> and in Skaltsa et al (2010, 2012) <doi:10.1002/bimj.200900294>, <doi:10.1002/sim.4369>.

This package provides a traceability focused tool created to simplify the data manipulation necessary to create clinical summaries.