This package provides density, distribution function, quantile function, and random generation for the Tweedie distribution under the compound Poisson-Gamma parameterisation with power parameter in (1, 2). The density is evaluated using the series expansion of Dunn and Smyth (2005) <doi:10.1007/s11222-005-4070-y>, implemented in C++ via Rcpp and RcppArmadillo for performance. A constructor compatible with the distributional package is also provided for use in tidy modelling workflows.

Longitudinal data offers insights into population changes over time but often requires a flexible structure, especially with varying follow-up intervals. Panel data is one way to store such records, though it adds complexity to analysis. The tvtools package for R simplifies exploring and analyzing panel data.

Generates a game of 2048 that can be played in the console. Supports grids of arbitrary sizes, undoing the last move, and resuming a game that was exited during the current session.

This package provides a shiny based interactive exploration framework for analyzing clinical trials data. teal currently provides a dynamic filtering facility and different data viewers. teal shiny applications are built using standard shiny modules.

Analyse data from longitudinal studies to characterise changes in values of semi-quantitative outcome variables within individual subjects, using high performance C++ code to enable rapid processing of large datasets. A flexible methodology is available for codifying these state transitions.

This package provides a game inspired by Tetris'. Opens a plot window device and starts a game of tetRys in it. Steer the tetrominos with the arrow keys, press Pause to pause and Esc to end the game.

Topological data analysis is a powerful tool for finding non-linear global structure in whole datasets. The main tool of topological data analysis is persistent homology, which computes a topological shape descriptor of a dataset called a persistence diagram. TDApplied provides useful and efficient methods for analyzing groups of persistence diagrams with machine learning and statistical inference, and these functions can also interface with other data science packages to form flexible and integrated topological data analysis pipelines.

R implementation of TFactS to predict which are the transcription factors (TFs), regulated in a biological condition based on lists of differentially expressed genes (DEGs) obtained from transcriptome experiments. This package is based on the TFactS concept by Essaghir et al. (2010) <doi:10.1093/nar/gkq149> and expands it. It allows users to perform TFactS'-like enrichment approach. The package can import and use the original catalogue file from the TFactS as well as users defined catalogues of interest that are not supported by TFactS (e.g., Arabidopsis).

This package provides a minimalistic, dependency-free alternative to roxygen2'. Reads # comment blocks above R functions and objects and turns them into .Rd documentation files and a NAMESPACE file. tinyroxygen is to roxygen2 what tinytest is to testthat', a lightweight alternative built with base R only, with no recursive dependencies to install. Roxygen stands for R documentation inspired by the Doxygen for C++ documentation tools.

Which uses Twitter APIs for the necessary data in sentiment analysis, acts as a middleware with the approved Twitter Application. A special access key is given to users who subscribe to the application with their Twitter account. With this special access key, the user defined keyword for sentiment analysis can be searched in twitter recent searches and results can be obtained( more information <https://github.com/hakkisabah/tsentiment> ). In addition, a service named tsentiment-services has been developed to provide all these operations ( for more information <https://github.com/hakkisabah/tsentiment-services> ). After the successful results obtained and in line with the permissions given by the user, the results of the analysis of the word cloud and bar graph saved in the user folder directory can be seen. In each analysis performed, the previous analysis visual result is deleted and this is the basic information you need to know as a practice rule. tsentiment package provides a free service that acts as a middleware for easy data extraction from Twitter, and in return, the user rate limit is reduced by 30 requests from the total limit and the remaining requests are used. These 30 requests are reserved for use in application analytics. For information about endpoints, you can refer to the limit information in the "GET search/tweets" row in the Endpoints column in the list at <https://developer.twitter.com/en/docs/twitter-api/v1/rate-limits>.

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>).

This package provides a set of tools for managing time-series data, with a particular emphasis on defining various frequency types such as daily and weekly. It also includes functionality for converting data between different frequencies.

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.).

Themes for ggplot2 are a convenient way to style plots. The hrbrthemes package contains a particularly nice one, but brings along a significant tail of dependencies. So this (currently experimental) package brings along just the theme_ipsum_rc theme using the Roboto Condensed font. Should the font not be installed on your system, see the help in the package hrbrthemes on how to install Roboto Condensed'. Note that hrbrthemes is now archived at CRAN.

The goal of trainR is to provide a simple interface to the National Rail Enquiries (NRE) systems. There are few data feeds available, the simplest of them is Darwin, which provides real-time arrival and departure predictions, platform numbers, delay estimates, schedule changes and cancellations. Other data feeds provide historical data, Historic Service Performance (HSP), and much more. trainR simplifies the data retrieval, so that the users can focus on their analyses. For more details visit <https://www.nationalrail.co.uk/46391.aspx>.

Interface to TensorFlow Datasets, a high-level library for building complex input pipelines from simple, re-usable pieces. See <https://www.tensorflow.org/guide> for additional details.

This package provides functions for the selection of thresholds for use in extreme value models, based mainly on the methodology in Northrop, Attalides and Jonathan (2017) <doi:10.1111/rssc.12159>. It also performs predictive inferences about future extreme values, based either on a single threshold or on a weighted average of inferences from multiple thresholds, using the revdbayes package <https://cran.r-project.org/package=revdbayes>. At the moment only the case where the data can be treated as independent identically distributed observations is considered.

Helper functions for creating, editing, and testing tutorials created with the learnr package. Provides a simple method for allowing students to download their answers to tutorial questions. For examples of its use, see the r4ds.tutorials package.

This package performs turning point and inflection point tests for U-shaped and inverse U-shaped relationships in regression models. Implements the Sasabuchi (1980) test as extended by Lind and Mehlum (2010) with support for quadratic, cubic, log-quadratic, and inverse functional forms. Features include delta-method standard errors, Fieller confidence intervals, Simonsohn (2018) two-lines test, and parametric bootstrap. Designed for post-estimation analysis of linear models, panel models, and quantile regression. References: Lind and Mehlum (2010) <doi:10.1111/j.1468-0084.2009.00569.x>; Sasabuchi (1980); Fieller (1954) <doi:10.1111/j.2517-6161.1954.tb00159.x>.

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.

This package provides a set of functions with a common framework for age-depth model management, stratigraphic visualization, and common statistical transformations. The focus of the package is stratigraphic visualization, for which ggplot2 components are provided to reproduce the scales, geometries, facets, and theme elements commonly used in publication-quality stratigraphic diagrams. Helpers are also provided to reproduce the exploratory statistical summaries that are frequently included on stratigraphic diagrams. See Dunnington et al. (2021) <doi:10.18637/jss.v101.i07>.

This package provides a lemmatized critical edition of the complete Pali Canon (Tipitaka), the canonical scripture of Theravadin Buddhism. Based on a five-witness collation of the Pali Text Society (PTS) edition (via GRETIL'), SuttaCentral', the Vipassana Research Institute (VRI) Chattha Sangayana edition, the Buddha Jayanti Tipitaka (BJT), and the Thai Royal Edition. All text is lemmatized using the Digital Pali Dictionary', grouping inflected forms by dictionary headword. Covers all three pitakas (Sutta, Vinaya, Abhidhamma) with 5,777 individual text units. The companion package tipitaka provides the original VRI edition data and Pali text tools. For background on the collation method, see Zigmond (2026) <https://github.com/dangerzig/tipitaka.critical>.

Computes a point pattern in R^2 or on a graph that is representative of a collection of many data patterns. The result is an approximate barycenter (also known as Fréchet mean or prototype) based on a transport-transform metric. Possible choices include Optimal SubPattern Assignment (OSPA) and Spike Time metrics. Details can be found in Müller, Schuhmacher and Mateu (2020) <doi:10.1007/s11222-020-09932-y>.

This package provides a mathematical optimization procedure in combination with statistical bootstrap for the estimation of the latent signals (sometimes called scores) informing the global consensus ranking (often named aggregation ranking). To solve mid/large-scale problems, users should install the gurobi optimiser (available from <https://www.gurobi.com/>).