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.

This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner "Titanic", summarized according to economic status (class), sex, age and survival. Whereas the base R Titanic data found by calling data("Titanic") is an array resulting from cross-tabulating 2201 observations, these data sets are the individual non-aggregated observations and formatted in a machine learning context with a training sample, a testing sample, and two additional data sets that can be used for deeper machine learning analysis. These data sets are also the data sets downloaded from the Kaggle competition and thus lowers the barrier to entry for users new to R or machine learing.

This package provides a modular package for simulating phylogenetic trees and species traits jointly. Trees can be simulated using modular birth-death parameters (e.g. changing starting parameters or algorithm rules). Traits can be simulated in any way designed by the user. The growth of the tree and the traits can influence each other through modifiers objects providing rules for affecting each other. Finally, events can be created to modify both the tree and the traits under specific conditions ( Guillerme, 2024 <DOI:10.1111/2041-210X.14306>).

This package provides utility functions for data analysis and scientific computing. Includes functions for logging, parallel processing, and other computational tasks to streamline workflows.

Fit, compare, and visualize Bayesian graphical vector autoregressive (GVAR) network models using Stan'. These models are commonly used in psychology to represent temporal and contemporaneous relationships between multiple variables in intensive longitudinal data. Fitted models can be compared with a test based on matrix norm differences of posterior point estimates to quantify the differences between two estimated networks. See also Siepe, Kloft & Heck (2024) <doi:10.31234/osf.io/uwfjc>.

This package provides functions to generate stop-word lists in 110 languages, in a way consistent across all the languages supported. The generated lists are based on the morphological tagset from the Universal Dependencies.

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>.

An implementation of the Thornley transport resistance plant growth model. The package can be used to simulate plant growth as forced by climate system variables. The package provides methods for formatting forcing variables, simulating growth dynamics and calibrating model parameters. For more information see Higgins et al. (2025) TTR.PGM: An R package for modelling the distributions and dynamics of plants using the Thornley transport resistance plant growth model. Methods in Ecology and Evolution. in press.

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 ts objects in R are managed using a very specific date format (in the form c(2022, 9) for September 2022 or c(2021, 2) for the second quarter of 2021, depending on the frequency, for example). We focus solely on monthly and quarterly series to manage the dates of ts objects. The general idea is to offer a set of functions to manage this date format without it being too restrictive or too imprecise depending on the rounding. This is a compromise between simplicity, precision and use of the basic stats functions for creating and managing time series (ts(), window()). Les objets ts en R sont gérés par un format de date très particulier (sous la forme c(2022, 9) pour septembre 2022 ou c(2021, 2) pour le deuxième trimestre 2021 selon la fréquence par exemple). On se concentre uniquement sur les séries mensuelles et trimestrielles pour gérer les dates des objets ts. Lidée générale est de proposer un ensemble de fonctions pour gérer ce format de date sans que ce soit trop contraignant ou trop imprécis selon les arrondis. Cest un compromis entre simplicité, précision et utilisation des fonctions du package stats de création et de gestion des séries temporelles (ts(), window()).

This package provides a model for the growth of self-limiting populations using three, four, or five parameter functions, which have wide applications in a variety of fields. The dependent variable in a dynamical modeling could be the population size at time x, where x is the independent variable. In the analysis of quantitative polymerase chain reaction (qPCR), the dependent variable would be the fluorescence intensity and the independent variable the cycle number. This package then would calculate the TWW cycle threshold.

This package implements harmonic analysis of tidal and sea-level data. Over 400 harmonic tidal constituents can be estimated, all with daily nodal corrections. Time-varying mean sea-levels can also be used.

Some accelerated three-term conjugate gradient algorithms implemented purely in R with the same user interface as optim(). The search directions and acceleration scheme are described in Andrei, N. (2013) <doi:10.1016/j.amc.2012.11.097>, Andrei, N. (2013) <doi:10.1016/j.cam.2012.10.002>, and Andrei, N (2015) <doi:10.1007/s11075-014-9845-9>. Line search is done by a hybrid algorithm incorporating the ideas in Oliveia and Takahashi (2020) <doi:10.1145/3423597> and More and Thuente (1994) <doi:10.1145/192115.192132>.

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.

Function for sparse regression on raw text, regressing a labeling vector onto a feature space consisting of all possible phrases.

This package contains several utility functions for manipulating tensor-valued data (centering, multiplication from a single mode etc.) and the implementations of the following blind source separation methods for tensor-valued data: tPCA', tFOBI', tJADE', k-tJADE', tgFOBI', tgJADE', tSOBI', tNSS.SD', tNSS.JD', tNSS.TD.JD', tPP and tTUCKER'.

Easily construct prompts and associated logic for interacting with large language models (LLMs). tidyprompt introduces the concept of prompt wraps, which are building blocks that you can use to quickly turn a simple prompt into a complex one. Prompt wraps do not just modify the prompt text, but also add extraction and validation functions that will be applied to the response of the LLM. This ensures that the user gets the desired output. tidyprompt can add various features to prompts and their evaluation by LLMs, such as structured output, automatic feedback, retries, reasoning modes, autonomous R function calling, and R code generation and evaluation. It is designed to be compatible with any LLM provider that offers chat completion.

This package provides extended data frames, with a special data frame column which contains two indexes, with potentially a nesting structure, and support for tibbles and methods for dplyr'.

Factor and autoregressive models for matrix and tensor valued time series. We provide functions for estimation, simulation and prediction. The models are discussed in Li et al (2021) <doi:10.48550/arXiv.2110.00928>, Chen et al (2020) <DOI:10.1080/01621459.2021.1912757>, Chen et al (2020) <DOI:10.1016/j.jeconom.2020.07.015>, and Xiao et al (2020) <doi:10.48550/arXiv.2006.02611>.

Constraint-based causal discovery using the PC algorithm while accounting for a partial node ordering, for example a partial temporal ordering when the data were collected in different waves of a cohort study. Andrews RM, Foraita R, Didelez V, Witte J (2021) <arXiv:2108.13395> provide a guide how to use tpc to analyse cohort data.

Return the first four moments, estimation of parameters and sample of the TSMSN distributions (Skew Normal, Skew t, Skew Slash or Skew Contaminated Normal).

This package provides a kernel of functions for programming time series methods in a way that is relatively independently of the representation of time. Also provides plotting, time windowing, and some other utility functions which are specifically intended for time series. See the Guide distributed as a vignette, or ?tframe.Intro for more details. (User utilities are in package tfplot.).

This package provides an R-interface to the TMDb API (see TMDb API on <https://developers.themoviedb.org/3/getting-started/introduction>). The Movie Database (TMDb) is a popular user editable database for movies and TV shows (see <https://www.themoviedb.org>).

Pacote para a analise de experimentos com um ou dois fatores com testemunhas adicionais conduzidos no delineamento inteiramente casualizado ou em blocos casualizados. "Package for the analysis of one or two-way experiments with additional controls conducted in a completely randomized design or in a randomized block design".