Topological correlation coefficient is used to identify dependencies between Time-Dependent Objects and is applicable to objects such as time series, chaotic systems, and dynamic networks.

For when your colors absolutely should not be excluded from the narrative.

Optimizers for torch deep learning library. These functions include recent results published in the literature and are not part of the optimizers offered in torch'. Prospective users should test these optimizers with their data, since performance depends on the specific problem being solved. The packages includes the following optimizers: (a) adabelief by Zhuang et al (2020), <arXiv:2010.07468>; (b) adabound by Luo et al.(2019), <arXiv:1902.09843>; (c) adahessian by Yao et al.(2021) <arXiv:2006.00719>; (d) adamw by Loshchilov & Hutter (2019), <arXiv:1711.05101>; (e) madgrad by Defazio and Jelassi (2021), <arXiv:2101.11075>; (f) nadam by Dozat (2019), <https://openreview.net/pdf/OM0jvwB8jIp57ZJjtNEZ.pdf>; (g) qhadam by Ma and Yarats(2019), <arXiv:1810.06801>; (h) radam by Liu et al. (2019), <arXiv:1908.03265>; (i) swats by Shekar and Sochee (2018), <arXiv:1712.07628>; (j) yogi by Zaheer et al.(2019), <https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization>.

Tests the hypothesis that variances are homogeneous or not using bootstrap. The procedure uses a variance-based statistic, and is derived from a normal-theory test. The test equivalently expressed the hypothesis as a function of the log contrasts of the population variances. A box-type acceptance region is constructed to test the hypothesis. See Cahoy (2010) \doi10.1016/j.csda.2010.04.012.

This package provides S3 vector types for functional data represented on grids, in spline bases, or via functional principal components. Supports arithmetic and summary methods, plotting, derivation, integration, smoothing, registration, and data import/export for these functional vectors. Includes data-wrangling tools for re-evaluation, subsetting, sub-assignment, zooming into sub-domains, and extracting functional features such as minima, maxima, and their locations. Enables joint analysis of functional and scalar variables by integrating functional vectors into standard data frames.

This package implements differential language analysis with statistical tests and offers various language visualization techniques for n-grams and topics. It also supports the text package. For more information, visit <https://r-topics.org/> and <https://www.r-text.org/>.

Bayesian Tensor Factorization for decomposition of tensor data sets using the trilinear CANDECOMP/PARAFAC (CP) factorization, with automatic component selection. The complete data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The method performs factorization for three-way tensor datasets and the inference is implemented with Gibbs sampling.

Makes data wrangling with ID-related aspects more comfortable. Provides functions that make it easy to inspect various subject-generated ID codes (SGIC) for plausibility. Also helps with inspecting other common identifiers, ensuring that your data stays clean and reliable.

Helps the user to build and register schema descriptions of disorganised (messy) tables. Disorganised tables are tables that are not in a topologically coherent form, where packages such as tidyr could be used for reshaping. The schema description documents the arrangement of input tables and is used to reshape them into a standardised (tidy) output format.

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 package provides a framework for high-dimensional mediation analysis using transfer learning. The main function TransHDM() integrates large-scale source data to improve the detection power of potential mediators in small-sample target studies. It addresses data heterogeneity via transfer regularization and debiased estimation while controlling the false discovery rate. The package also includes utilities for data generation (gen_simData_homo(), gen_simData_hetero()), baseline methods such as lasso() and dblasso(), sure independence screening via SIS(), and model diagnostics through source_detection(). The methodology is described in Pan et al. (2025) <doi:10.1093/bib/bbaf460>.

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

This package provides a full-featured client for the Telegram MTProto protocol (<https://core.telegram.org/api>), enabling programmatic access to Telegram chats, channels, messages, media, and stories. Implements binary encoding and decoding of the Telegram TL (Type Language) schema, authentication (including two-factor), encrypted transport, and high-level helpers for downloading channel history and reactions at scale. Intended for social-science research and data collection tasks that require direct API access rather than the Bot API'.

This package provides a tool to obtain tumor growth rates from clinical trial patient data. Output includes individual and summary data for tumor growth rate estimates as well as optional plots of the observed and predicted tumor quantity over time.

The functions needed to perform tight clustering Algorithm.

Fits temperature response models to rate measurements taken at different temperatures. Etienne Low-Decarie,Tobias G. Boatman, Noah Bennett,Will Passfield,Antonio Gavalas-Olea,Philipp Siegel, Richard J. Geider (2017) <doi:10.1002/ece3.3576> .

This package provides functions to support economic modelling in R based on the methods of the Dutch guideline for economic evaluations in healthcare <https://www.zorginstituutnederland.nl/documenten/2024/01/16/richtlijn-voor-het-uitvoeren-van-economische-evaluaties-in-de-gezondheidszorg>, CBS data <https://www.cbs.nl/>, and OECD data <https://www.oecd.org/en.html>.

Multiscale multifractal analysis (MMA) (GieraÅ towski et al., 2012)<DOI:10.1103/PhysRevE.85.021915> is a time series analysis method, designed to describe scaling properties of fluctuations within the signal analyzed. The main result of this procedure is the so called Hurst surface h(q,s) , which is a dependence of the local Hurst exponent h (fluctuation scaling exponent) on the multifractal parameter q and the scale of observation s (data window width).

Enhances koRpus text object classes and methods to also support large corpora. Hierarchical ordering of corpus texts into arbitrary categories will be preserved. Provided classes and methods also improve the ability of using the koRpus package together with the tm package. To ask for help, report bugs, suggest feature improvements, or discuss the global development of the package, please subscribe to the koRpus-dev mailing list (<https://korpusml.reaktanz.de>).

Utilities to retrieve and tidy U.S. macroeconomic data series from public government data providers. Functions streamline access to series from the Federal Reserve Bank of St. Louis Federal Reserve Economic Data (FRED), the Bureau of Labor Statistics flat files, and the Bureau of Economic Analysis National Income and Product Accounts tables, then return consistent, tidy data frames ready for modeling and graphics. The package includes helpers for date alignment, log-linear projections, and common macro diagnostics, along with convenience plot builders for quick publication-quality charts.

Unsupervised text tokenizer focused on computational efficiency. Wraps the YouTokenToMe library <https://github.com/VKCOM/YouTokenToMe> which is an implementation of fast Byte Pair Encoding (BPE) <https://aclanthology.org/P16-1162/>.

Several datasets which describe the chef contestants in Top Chef, the challenges that they compete in, and the results of those challenges. This data is useful for practicing data wrangling, graphing, and analyzing how each season of Top Chef played out.

This package provides a wrapper for The Cancer Imaging Archive's REST API. The Cancer Imaging Archive (TCIA) hosts de-identified medical images of cancer available for public download, as well as rich metadata for each image series. TCIA provides a REST API for programmatic access to the data. This package provides simple functions to access each API endpoint. For more information, see <https://github.com/pamelarussell/TCIApathfinder> and TCIA's website.

The tabularmap is one of the visualization methods for efficiently displaying data consisting of multiple elements by tiling them. When dealing with geospatial, it corrects for differences in visibility between areas.