Build display tables from tabular data with an easy-to-use set of functions. With its progressive approach, we can construct display tables with a cohesive set of table parts. Table values can be formatted using any of the included formatting functions. Footnotes and cell styles can be precisely added through a location targeting system. The way in which gt handles things for you means that you don't often have to worry about the fine details.

Generalized Turnbull's estimator proposed by Dehghan and Duchesne (2011).

Insert tables created by the gt R package into Microsoft Word documents. This gives users the ability to add to their existing word documents the tables made in gt using the familiar officer package and syntax from the officeverse'.

This package provides a collection of functions for testing randomness (or mutual independence) in linear and circular data as proposed in Gehlot and Laha (2025a) <doi:10.48550/arXiv.2506.21157> and Gehlot and Laha (2025b) <doi:10.48550/arXiv.2506.23522>, respectively.

Implementation of the GTE (Group Technical Effects) model for single-cell data. GTE is a quantitative metric to assess batch effects for individual genes in single-cell data. For a single-cell dataset, the user can calculate the GTE value for individual features (such as genes), and then identify the highly batch-sensitive features. Removing these highly batch-sensitive features results in datasets with low batch effects.

Computes the probability density, survival function, the hazard rate functions and generates random samples from the GTDL distribution given by Mackenzie, G. (1996) <doi:10.2307/2348408>. The likelihood estimates, the randomized quantile (Louzada, F., et al. (2020) <doi:10.1109/ACCESS.2020.3040525>) residuals and the normally transformed randomized survival probability (Li,L., et al. (2021) <doi:10.1002/sim.8852>) residuals are obtained for the GTDL model.

This package creates tables suitable for regulatory agency submission by leveraging the gtsummary package as the back end. Tables can be exported to HTML, Word, PDF and more. Highly customized outputs are available by utilizing existing styling functions from gtsummary as well as custom options designed for regulatory tables.

This package provides a convenient R interface to the Genotype-Tissue Expression (GTEx) Portal API. The GTEx project is a comprehensive public resource for studying tissue-specific gene expression and regulation in human tissues. Through systematic analysis of RNA sequencing data from 54 non-diseased tissue sites across nearly 1000 individuals, GTEx provides crucial insights into the relationship between genetic variation and gene expression. This data is accessible through the GTEx Portal API enabling programmatic access to human gene expression data. For more information on the API, see <https://gtexportal.org/api/v2/redoc>.

Quantitative trait loci mapping and genome wide association analysis are used to find candidate molecular marker or region associated with phenotype based on linkage analysis and linkage disequilibrium. Gene expression quantitative trait loci mapping is used to find candidate molecular marker or region associated with gene expression. In this package, we applied the method in Liu W. (2011) <doi:10.1007/s00122-011-1631-7> and Gusev A. (2016) <doi:10.1038/ng.3506> to genome and transcriptome wide association study, which is aimed at revealing the association relationship between phenotype and molecular markers, expression levels, molecular markers nested within different related expression effect and expression effect nested within different related molecular marker effect. F test based on full and reduced model are performed to obtain p value or likelihood ratio statistic. The best linear model can be obtained by stepwise regression analysis.

Gtable is a collection of tools to make it easier to work with "tables" of grobs.

This package provides an object for plotting GRanges, RleList, UCSC file formats, and ffTrack objects in multi-track panels.

Four graph-based tests are provided for testing whether two samples are from the same distribution. It works for both continuous data and discrete data.

This package contains a collection of various functions to assist in R programming, such as tools to assist in developing, updating, and maintaining R and R packages, calculating the logit and inverse logit transformations, tests for whether a value is missing, empty or contains only NA and NULL values, and many more.

This package provides tools for the development of packages related to General Transit Feed Specification (GTFS) files. Establishes a standard for representing GTFS feeds using R data types. Provides fast and flexible functions to read and write GTFS feeds while sticking to this standard. Defines a basic gtfs class which is meant to be extended by packages that depend on it. And offers utility functions that support checking the structure of GTFS objects.

This package provides tools to streamline the extraction, processing, and visualization of Computable General Equilibrium (CGE) results from GTAP models. Designed for compatibility with both .har and .sl4 files, the package enables users to automate data preparation, apply mapping metadata, and generate high-quality plots and summary tables with minimal coding. GTAPViz supports flexible export options (e.g., Text, CSV, Stata', or Excel formats). This facilitates efficient post-simulation analysis for economic research and policy reporting. Includes helper functions to filter, format, and customize outputs with reproducible styling.

This package provides additional functions for creating beautiful tables with gt'. The functions are generally wrappers around boilerplate or adding opinionated niche capabilities and helpers functions.

An interface for retrieving and displaying the information returned online by Google Trends is provided. Trends (number of hits) over the time as well as geographic representation of the results can be displayed.

Convert general transit feed specification (GTFS) data to global positioning system (GPS) records in data.table format. It also has some functions to subset GTFS data in time and space and to convert both representations to simple feature format.

Genome level Trellis graph visualizes genomic data conditioned by genomic categories (e.g. chromosomes). For each genomic category, multiple dimensional data which are represented as tracks describe different features from different aspects. This package provides high flexibility to arrange genomic categories and to add self-defined graphics in the plot.

Generalizes application of gray-level co-occurrence matrix (GLCM) metrics to objects outside of images. The current focus is to apply GLCM metrics to the study of biological networks and fitness landscapes that are used in studying evolutionary medicine and biology, particularly the evolution of cancer resistance. The package was developed as part of the author's publication in Physics in Medicine and Biology Barker-Clarke et al. (2023) <doi:10.1088/1361-6560/ace305>. A general reference to learn more about mathematical oncology can be found at Rockne et al. (2019) <doi:10.1088/1478-3975/ab1a09>.

Utility functions to read, manipulate, analyse and write transit feeds in the General Transit Feed Specification (GTFS) data format.

This package provides tools to measure the reliability of an Information Retrieval test collection. It allows users to estimate reliability using Generalizability Theory and map those estimates onto well-known indicators such as Kendall tau correlation or sensitivity.

Understanding how features influence a specific response variable becomes crucial in classification problems, with applications ranging from medical diagnosis to customer behavior analysis. This packages provides tools to compute such an influence measure grounded on game theory concepts. In particular, the influence measures presented in Davila-Pena, Saavedra-Nieves, and Casas-Méndez (2024) <doi:10.48550/arXiv.2408.02481> can be obtained.

This package creates presentation-ready tables summarizing data sets, regression models, and more. The code to create the tables is concise and highly customizable. Data frames can be summarized with any function, e.g. mean(), median(), even user-written functions. Regression models are summarized and include the reference rows for categorical variables. Common regression models, such as logistic regression and Cox proportional hazards regression, are automatically identified and the tables are pre-filled with appropriate column headers.