The Ultimate Guide to the timereg Package in R (2024)

• R Package Guides

Table of contents

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In this section, we’ll delve into the fundamental aspects and key features of the package.

The timereg package is a comprehensive tool for the modeling and estimation of time-to-event data. It offers a suite of functions for survival analysis, including the Cox proportional hazards model and the Aalen additive hazards model.

• Title: Flexible Regression Models for Survival Data
• Description: Programs for Martinussen and Scheike (2006), `Dynamic Regression Models for Survival Data', Springer Verlag. Plus more recent developments. Additive survival model, semiparametric proportional odds model, fast cumulative residuals, excess risk models and more. Flexible competing risks regression including GOF-tests. Two-stage frailty modelling. PLS for the additive risk model. Lasso in the 'ahaz' package.
• Author: Thomas Scheike with contributions from Torben Martinussen, Jeremy Silver and Klaus Holst
• Maintainer: Thomas Scheike

• Functions: Functions play a crucial role in R packages. They allow you to perform specific tasks and computations efficiently. To identify the functions in the timereg package, you can use the ls("package:timereg") function.
• Datasets: Many R packages include built-in datasets that you can use to familiarize yourself with their functionalities. To identify any built-in datasets in the timereg package, you can use the data(package = "timereg") function.
• Vignettes: R vignettes are documents that include examples for using a package. To view the list of available vignettes, you can use the vignette(package = "timereg") function.
• Citation information: Citing R packages in your publications is important as it recognizes the contributions of the developers. To find the citation information for the timereg package in the R console, you can use the citation("timereg") function.

• License type: GPL (>= 2). For license details, visit the Open Source Initiative website.
• Compilation requirements: Some R packages include internal code that must be compiled for them to function correctly. The timereg package has compilation requirements.
• Required dependencies: A required dependency refers to another package that is essential for the functioning of the main package. The timereg package has the following required dependencies: R (>= 2.15), survival.
• Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The timereg package has the following suggested dependencies: mets.
• External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The timereg package does not use any external sources.
• Imported packages: Importing packages allows developers to leverage existing code and functionalities without having to reinvent the wheel. The timereg package has the following imported packages: lava, numDeriv, stats, graphics, grDevices, utils, methods.
• Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The timereg package has no enhancements.

# Replace "packageName" with the name of the R package you want to explore # Get details and citation for the R package package_details <- packageDescription("packageName") package_citation <- citation("packageName") # Print the package details and citation print(package_details) print(package_citation)

• The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("timereg") function to retrieve detailed information, examples, and usage instructions. Alternatively, you can use the ? operator as a shortcut.

• Package website: The timereg package has a dedicated website. You can visit: https://github.com/scheike/timereg.

• Developer support: You can email Thomas Scheike . For contact information, visit our R community directory.

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