The tdROC package facilitates the estimation of time-dependent ROC (Receiver Operating Characteristic) curves and the Area Under the time-dependent ROC Curve (AUC) in the context of survival data, accommodating scenarios with right censored data and the option to account for competing risks. In addition to the ROC/AUC estimation, the package also estimates time-dependent Brier score and survival difference. Confidence intervals of various estimated quantities can be obtained from bootstrap. The package also offers plotting functions for visualizing time-dependent ROC curves.

Assists in the TOPSIS analysis process, designed to return at the end of the answer of the TOPSIS multicriteria analysis, a ranking table with the best option as the analysis proposes. TOPSIS is basically a technique developed by Hwang and Yoon in 1981, starting from the point that the best alternative should be closest to the positive ideal solution and farthest from the negative one, based on several criteria to result in the best benefit. (LIU, H. et al., 2019) <doi:10.1016/j.agwat.2019.105787>.

Create HTML tables of descriptive statistics, as one would expect to see as the first table (i.e. "Table 1") in a medical/epidemiological journal article.

This package provides tidyverse methods for wrangling and analyzing Earth Engine <https://earthengine.google.com/> data. These methods help the user with filtering, joining and summarising Earth Engine image collections.

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.

Create rich and fully interactive timeline visualizations. Timelines can be included in Shiny apps or R markdown documents. timevis includes an extensive API to manipulate a timeline after creation, and supports getting data out of the visualization into R. Based on the vis.js Timeline JavaScript library.

Increases the ease of implementing a temporally-explicit modeling methodology when building ecological niche and species distribution models. Provides functions to assist with three major steps of temporally-explicit models: (i) preprocessing species and environmental data and generating suitable background or pseudoabsence data, (ii) building a niche model and generating temporally-explicit predictions from that model, and (iii) model postprocessing to explore spatiotemporal trends in model predictions. Methodological and theoretical foundations are described in Ingenloff and Peterson (2021) <doi:10.1111/2041-210X.13564>, Franklin (2010, ISBN:9780521700023), Peterson et al. (2011, ISBN:9780691136882), Blonder (2018) <doi:10.1111/ecog.03187>, Senay et al. (2013) <doi:10.1371/journal.pone.0071218>, and Li and Zhang (2024) <doi:10.48550/arXiv.2404.05933>.

Models the direction of the maximum horizontal stress using relative plate motion parameters. Statistical algorithms to evaluate the modeling results compared with the observed data. Provides plots to visualize the results. Methods described in Stephan et al. (2023) <doi:10.1038/s41598-023-42433-2> and Wdowinski (1998) <doi:10.1016/S0079-1946(98)00091-3>.

Table, Listings, and Graphs (TLG) library for common outputs used in clinical trials.

Render clinical submission tables, listings, and figures to RTF', LaTeX', Typst', HTML', PDF', and DOCX from pre-summarised data frames, with no external Java or SAS dependency. Features include decimal alignment via font metrics, multi-level column headers with passthrough leaves, predicate-targeted cell styling, footnotes, group-aware pagination, and figures that wrap a plot or image in the same page chrome as a table. Built for Clinical Data Interchange Standards Consortium (CDISC) Analysis Data Model (ADaM) workflows and regulatory submissions to agencies such as the Food and Drug Administration (FDA), European Medicines Agency (EMA), and Pharmaceuticals and Medical Devices Agency (PMDA).

Compute arbitrary non-parametric bootstrap statistics on data in tidy data frames.

Create additional rows and columns on broom::tidy() output to allow for easier control on categorical parameter estimates.

This package provides a tool to help create shiny apps for selecting and annotating elements of images. Users must supply images, questions, and answer choices. The user interface is a dynamic shiny app, that displays the images and questions and answer choices. The data generated can be saved to a file that can be used for subsequent analysis. The original purpose was to annotate still images from tennis video for face recognition and emotion detection purposes.

We described a novel Topology-based pathway enrichment analysis, which integrated the global position of the nodes and the topological property of the pathways in Kyoto Encyclopedia of Genes and Genomes Database. We also provide some functions to obtain the latest information about pathways to finish pathway enrichment analysis using this method.

This package provides a unified estimation procedure for the analysis of right censored data using linear transformation models. An introduction can be found in Jie Zhou et al. (2022) <doi:10.18637/jss.v101.i09>.

This package provides a comprehensive and user-friendly interface for accessing, manipulating, and analyzing country-level data from around the world. It allows users to retrieve detailed information on countries, including names, regions, continents, populations, currencies, calling codes, and more, all in a tidy data format. The package is designed to work seamlessly within the tidyverse ecosystem, making it easy to filter, arrange, and visualize country-level data in R.

Collect your data on digital marketing campaigns from Taboola using the Windsor.ai API <https://windsor.ai/api-fields/>.

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.

This package provides utility functions to help researchers implement best practices for their coding projects. Includes tools for reading and cleaning data files, initializing R projects with a standard folder structure and optional YAML configuration, creating Quarto documents from reproducible templates with optional sample data and custom styling, detecting the execution context across interactive, Quarto', and script-based workflows, splitting data frames into group-level output files, applying analysis functions to each group with optional parallel execution, and rendering syntactic tree diagrams as standalone PNG images via Typst'.

This package provides a set of tools for descriptive and predictive analysis of time series data. That includes functions for interactive visualization of time series objects and as well utility functions for automation time series forecasting.

This package provides wrapper functions to the multiple marginal model function mmm() of package multcomp to implement the trend test of Tukey, Ciminera and Heyse (1985) <DOI:10.2307/2530666> for general parametric models.

This package provides a teal_data class as a unified data model for teal applications focusing on reproducibility and relational data.

Support functions and datasets to facilitate the analysis of linguistic data. The current focus is on the calculation of corpus-linguistic dispersion measures as described in Gries (2021) <doi:10.1007/978-3-030-46216-1_5> and Soenning (2025) <doi:10.3366/cor.2025.0326>. The most commonly used parts-based indices are implemented, including different formulas and modifications that are found in the literature, with the additional option to obtain frequency-adjusted scores. Dispersion scores can be computed based on individual count variables or a term-document matrix.

Computes the implicit weights attached to two-way fixed effects regressions, as well as summary measures of these regressions robustness to heterogeneous treatment effects. Based on de Chaisemartin and D'Haultfoeuille (2020) <DOI:10.1257/aer.20181169>.