This package provides support for a variety of spatial data sources in tmap', including remote, tiled, and streaming formats. Enables the use of external vector and raster data without requiring full data import, facilitating efficient visualization workflows.

Disaggregates low frequency time series data to higher frequency series. Implements the following methods for temporal disaggregation: Boot, Feibes and Lisman (1967) <DOI:10.2307/2985238>, Chow and Lin (1971) <DOI:10.2307/1928739>, Fernandez (1981) <DOI:10.2307/1924371> and Litterman (1983) <DOI:10.2307/1391858>.

This package provides a user-friendly R data package that is intended to make Turkish higher education statistics more accessible.

Language specific cardinal to ordinal number conversion.

This package provides a tidy toolkit for working with the emoji in any text column, such as social-media posts, product reviews, chat logs or survey responses. Unicode is awkward to handle and not every code point is an emoji, which makes emoji statistics fiddly to obtain. tidyEmoji extracts, counts, categorises and sentiment-scores emoji with grapheme-aware detection (so skin-tone and multi-person sequences stay intact), returning tidy data frames that slot straight into a tidyverse workflow. The bundled emoji sentiment lexicon is from the Emoji Sentiment Ranking of Kralj Novak et al. (2015) <doi:10.1371/journal.pone.0144296>, released under CC BY-SA 4.0.

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.

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>.

Estimates the time-varying (tv) parameters of the GARCH(1,1) model, enabling the modeling of non-stationary volatilities by allowing the model parameters to change gradually over time. The estimation and prediction processes are facilitated through the application of the Kalman filter and state-space equations. This package supports the estimation of tv parameters for various deterministic functions, which can be identified through exploratory analysis of different time periods or segments of return data. The methodology is grounded in the framework presented by Ferreira et al. (2017) <doi:10.1080/00949655.2017.1334778>.

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'.

This package provides functions for assigning taxonomy to NCBI accession numbers and taxon IDs based on NCBI's accession2taxid and taxdump files. This package allows the user to download NCBI data dumps and create a local database for fast and local taxonomic assignment.

This package provides methods and tools for generating forecasts at different temporal frequencies using a hierarchical time series approach.

Density, distribution function, quantile function, and random generating function of the Toppâ Leone Garima distribution based on Boonmeekham, A., Supapakorn, T., & Bodhisuwan, W. (2025)<doi:10.1134/S1995080225608471>. In addition, maximum likelihood estimation for the Toppâ Leone Garima distribution is provided.

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.

The library allows to perform a multivariate time series classification based on the use of Discrete Wavelet Transform for feature extraction, a step wise discriminant to select the most relevant features and finally, the use of a linear or quadratic discriminant for classification. Note that all these steps can be done separately which allows to implement new steps. Velasco, I., Sipols, A., de Blas, C. S., Pastor, L., & Bayona, S. (2023) <doi:10.1186/S12938-023-01079-X>. Percival, D. B., & Walden, A. T. (2000,ISBN:0521640687). Maharaj, E. A., & Alonso, A. M. (2014) <doi:10.1016/j.csda.2013.09.006>.

Introduction of qenv S4 class, that facilitates code execution and reproducibility in teal applications.

Fast, reproducible detection and quantitative analysis of tertiary lymphoid structures (TLS) in multiplexed tissue imaging. Implements Independent Component Analysis Trace (ICAT) index, local Ripley's K scanning, automated K Nearest Neighbor (KNN)-based TLS detection, and T-cell clusters identification as described in Amiryousefi et al. (2025) <doi:10.1101/2025.09.21.677465>.

TidyTuesday is a project by the Data Science Learning Community in which they post a weekly dataset in a public data repository (<https://github.com/rfordatascience/tidytuesday>) for people to analyze and visualize. This package provides the tools to easily download this data and the description of the source.

Detection of outliers in time series following the Chen and Liu (1993) <DOI:10.2307/2290724> procedure. Innovational outliers, additive outliers, level shifts, temporary changes and seasonal level shifts are considered.

When plotting treated-minus-control differences, after-minus-before changes, or difference-in-differences, the ttrans() function symmetrically transforms the positive and negative tails to aid plotting. The package includes an observational study with three control groups and an unaffected outcome; see Rosenbaum (2022) <doi:10.1080/00031305.2022.2063944>.

This package provides methods for generating .dat files for use with the AMPL software using spatial data, particularly rasters. It includes support for various spatial data formats and different problem types. By automating the process of generating AMPL datasets, this package can help streamline optimization workflows and make it easier to solve complex optimization problems. The methods implemented in this package are described in detail in a publication by Fourer et al. (<doi:10.1287/mnsc.36.5.519>).

This package provides a terribly-simple data base for numeric time series, written purely in R, so no external database-software is needed. Series are stored in plain-text files (the most-portable and enduring file type) in CSV format. Timestamps are encoded using R's native numeric representation for Date'/'POSIXct', which makes them fast to parse, but keeps them accessible with other software. The package provides tools for saving and updating series in this standardised format, for retrieving and joining data, for summarising files and directories, and for coercing series from and to other data types (such as zoo series).

This package provides a wrapper to a set of algorithms designed to recognise positional cues present in hierarchical for-human Tables (which would normally be interpreted visually by the human brain) to decompose, then reconstruct the data into machine-readable LongForm Dataframes.

Test your data! An extension of the testthat unit testing framework with a family of functions and reporting tools for checking and validating data frames.

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.