Carries out analyses of two-way tables with one observation per cell, together with graphical displays for an additive fit and a diagnostic plot for removable non-additivity via a power transformation of the response. It implements Tukey's Exploratory Data Analysis (1973) <ISBN: 978-0201076165> methods, including a 1-degree-of-freedom test for row*column non-additivity', linear in the row and column effects.

Simplifies access to Tunisian government open data from <https://data.gov.tn/fr/>. Queries datasets by theme, author, or keywords, retrieves metadata, and gets structured results ready for analysis; all through the official CKAN API.

Estimate the transition diagnostic classification model (TDCM) described in Madison & Bradshaw (2018) <doi:10.1007/s11336-018-9638-5>, a longitudinal extension of the log-linear cognitive diagnosis model (LCDM) in Henson, Templin & Willse (2009) <doi:10.1007/s11336-008-9089-5>. As the LCDM subsumes many other diagnostic classification models (DCMs), many other DCMs can be estimated longitudinally via the TDCM. The TDCM package includes functions to estimate the single-group and multigroup TDCM, summarize results of interest including item parameters, growth proportions, transition probabilities, transitional reliability, attribute correlations, model fit, and growth plots.

General framework to organize data, methods, and results used in reproducible scientific analyses. A TAF analysis consists of four scripts (data.R, model.R, output.R, report.R) that are run sequentially. Each script starts by reading files from a previous step and ends with writing out files for the next step. Convenience functions are provided to version control the required data and software, run analyses, clean residues from previous runs, manage files, manipulate tables, and produce figures. With a focus on stability and reproducible analyses, the TAF package comes with no dependencies. TAF forms a base layer for the icesTAF package and other scientific applications.

The goal of this package will be to provide a simple interface for automatic machine learning that fits the tidymodels framework. The intention is to work for regression and classification problems with a simple verb framework.

This package provides functions that compute predictions from Generalised Additive Models (GAMs) fitted with mgcv and return them as a tibble. These can be plotted with a generic plot()-method that uses ggplot2 or plotted as any other data frame. The main function is predict_gam().

Interface to TensorFlow IO', Datasets and filesystem extensions maintained by `TensorFlow SIG-IO` <https://github.com/tensorflow/community/blob/master/sigs/io/CHARTER.md>.

Determine the path of the executing script. Compatible with several popular GUIs: Rgui', RStudio', Positron', VSCode', Jupyter', Emacs', and Rscript (shell). Compatible with several functions and packages: source()', sys.source()', debugSource() in RStudio', compiler::loadcmp()', utils::Sweave()', box::use()', knitr::knit()', plumber::plumb()', shiny::runApp()', package:targets', and testthat::source_file()'.

Fits a wide variety of multivariate spatio-temporal models with simultaneous and lagged interactions among variables (including vector autoregressive spatio-temporal ('VAST') dynamics) for areal, continuous, or network spatial domains. It includes time-variable, space-variable, and space-time-variable interactions using dynamic structural equation models ('DSEM') as expressive interface, and the mgcv package to specify splines via the formula interface. See Thorson et al. (2025) <doi:10.1111/geb.70035> for more details.

This package provides a constrained two-dimensional Delaunay triangulation package providing both triangulation and generation of voronoi mosaics of irregular spaced data. Please note that most of the functions are now also covered in package interp, which is a re-implementation from scratch under a free license based on a different triangulation algorithm.

Query Wikidata API <https://www.wikidata.org/wiki/Wikidata:Main_Page> with ease, get tidy data frames in response, and cache data in a local database.

Computation of stopping boundaries for a single-arm trial using a Bayesian criterion; i.e., for each m<=n (n= total patient number of the trial) the smallest number of observed toxicities is calculated leading to the termination of the trial/accrual according to the specified criteria. The probabilities of stopping the trial/accrual at and up until (resp.) the m-th patient (m<=n) is also calculated. This design is more conservative than the frequentist approach (using Clopper Pearson CIs) which might be preferred as it concerns safety.See also Aamot et.al.(2010) "Continuous monitoring of toxicity in clinical trials - simulating the risk of stopping prematurely" <doi:10.5414/cpp48476>.

Fast calculation of the Subtree Prune and Regraft (SPR), Tree Bisection and Reconnection (TBR) and Replug distances between unrooted trees, using the algorithms of Whidden and Matsen (2017) <doi:10.48550/arXiv.1511.07529>.

This package provides a collection of functions for generating frequency tables and cross-tabulations of categorical variables. The resulting tables can be exported to various formats (Excel, PDF, HTML, etc.) with extensive formatting and layout customization options.

The companion package that provides all the datasets used in the book "Data Integration, Manipulation and Visualization of Phylogenetic Trees" by Guangchuang Yu (2022, ISBN:9781032233574).

Theme and colour palettes for The Globe and Mail's graphics. Includes colour and fill scale functions, colour palette helpers and a Globe-styled ggplot2 theme object.

This package implements rank preserving structural failure time model (RPSFTM), iterative parameter estimation (IPE), inverse probability of censoring weights (IPCW), marginal structural model (MSM), simple two-stage estimation (TSEsimp), and improved two-stage estimation with g-estimation (TSEgest) methods for treatment switching in randomized clinical trials.

The ESTIMATE package infers tumor purity from expression data as a function of immune and stromal infiltrate, but requires writing of intermediate files, is un-pipeable, and performs poorly when presented with modern datasets with current gene symbols. tidyestimate a fast, tidy, modern reimagination of ESTIMATE (2013) <doi:10.1038/ncomms3612>.

This package provides methods for generating modelled parametric Tropical Cyclone (TC) spatial hazard fields and time series output at point locations from TC tracks. R's compatibility to simply use fast cpp code via the Rcpp package and the wide range spatial analysis tools via the terra package makes it an attractive open source environment to study TCs'. This package estimates TC vortex wind and pressure fields using parametric equations originally coded up in python by TCRM <https://github.com/GeoscienceAustralia/tcrm> and then coded up in Cuda cpp by TCwindgen <https://github.com/CyprienBosserelle/TCwindgen>.

Cooperative game theory models decision-making situations in which a group of agents, called players, may achieve certain benefits by cooperating to reach an optimal outcome. It has great potential in different fields, since it offers a scenario to analyze and solve problems in which cooperation is essential to achieve a common goal. The TUGLab (Transferable Utility Games Laboratory) R package contains a set of scripts that could serve as a helpful complement to the books and other materials used in courses on cooperative game theory, and also as a practical tool for researchers working in this field. The TUGLab project was born in 2006 trying to highlight the geometrical aspects of the theory of cooperative games for 3 and 4 players. TUGlabWeb is an online platform on which the basic functions of TUGLab are implemented, and it is being used all over the world as a resource in degree, master's and doctoral programs. This package is an extension of the first versions and enables users to work with games in general (computational restrictions aside). The user can check properties of games, compute well-known games and calculate several set-valued and single-valued solutions such as the core, the Shapley value, the nucleolus or the core-center. The package also illustrates how the Shapley value flexibly adapts to various cooperative game settings, including weighted players and coalitions, a priori unions, and restricted communication structures. In keeping with the original philosophy of the first versions, special emphasis is placed on the graphical representation of the solution concepts for 3 and 4 players.

Estimates heterogeneous treatment effects using tidy semantics on experimental or observational data. Methods are based on the doubly-robust learner of Kennedy (2023) <doi:10.1214/23-EJS2157>. You provide a simple recipe for what machine learning algorithms to use in estimating the nuisance functions and tidyhte will take care of cross-validation, estimation, model selection, diagnostics and construction of relevant quantities of interest about the variability of treatment effects.

This package provides tools for timescale decomposition of the classic variance ratio of community ecology. Tools are as described in Zhao et al (in prep), extending commonly used methods introduced by Peterson et al (1975) <doi: 10.2307/1936306>.

This package provides an intuitive interface for working with the competing risk endpoints. The package wraps the cmprsk package, and exports functions for univariate cumulative incidence estimates and competing risk regression. Methods follow those introduced in Fine and Gray (1999) <doi:10.1002/sim.7501>.

This package provides a comprehensive suite of statistical tools for analyzing, simulating, and computing properties of the Topp-Leone Cauchy Rayleigh (TLCAR) distribution, a versatile distribution amalgamating features of the Topp-Leone, Cauchy, and Rayleigh distributions, ideal for modeling intricate, heterogeneous data across scientific domains. See Atchadé, M.N., Bogninou, M.J., and Djibril, A.M. (2023) <doi:10.1007/s44199-023-00066-4> and Atchadé, M.N., Bogninou, M.J., and Djibril, A.M. (2024) <doi:10.1007/s44199-023-00069-1> for further insights.