The Ultimate Guide to the PBRF Package in R
Explore valuable documentation and insights to make the most of the PBRF package in R. Get ready to unlock the full potential of the PBRF package!
Learn R for Free with DataCamp
Master in-demand skills with DataCamp's interactive courses
Start Learning NowAs our partner, DataCamp may pay us a commission at no cost to you for purchases made through this link.
AI-powered R programming assistant
Have questions about the PBRF package? Get quick and helpful answers from our cutting-edge AI-powered assistant.
Advance Your Career with Udacity
Take your data and AI skills to the next level with industry-recognized Nanodegree programs
Explore CoursesAs our partner, Udacity may pay us a commission at no cost to you for purchases made through this link.
What is the PBRF package?
In this section, we’ll delve into the fundamental aspects and key features of the package.
The PBRF package in R is designed to implement Probabilistic Boolean Rule Filtering (PBRF). This method can be used for feature selection in high-dimensional binary datasets, making this package a valuable tool for bioinformatics research.
- Title: The Probability of Being in Response Function and Its Variance Estimates
- Description: Provides three ways to estimate the probability of being in response function (PBRF) The estimates are presented in Tsai, Luo and Crowley (2017) .
- Author: Xiaodong Luo [aut, cre], Sanofi [cph]
- Maintainer: Xiaodong Luo
How to install the PBRF package?
In this section, we’ll walk you through the process of installing and loading the PBRF package. By following these steps, you can seamlessly add new functions, datasets, and other resources to your R environment for a more robust workflow.
Upskill Your Data Science Team with O'Reilly
Access expert-led courses and resources for R programming and beyond
Explore O'Reilly's LibraryAs our partner, O'Reilly may pay us a commission at no cost to you for purchases made through this link.
What package information should you know?
In this section, we’ll go over the technical aspects of the PBRF package.
Key features
- Datasets: Many R packages include built-in datasets that you can use to familiarize yourself with their functionalities. To identify built-in datasets. To identify the datasets for the PBRF package, visit our database of R datasets.
- Vignettes: R vignettes are documents that include examples for using a package. To view the list of available vignettes for the PBRF package, you can visit our visit our database of R vignettes.
- Citation: Citing R packages in your publications is important as it recognizes the contributions of the developers. To find citation information for the PBRF package, visit our database of R package citations.
Technical details
- License type: NA. 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. NA
- Required dependencies: A required dependency refers to another package that is essential for the functioning of the main package. The PBRF package has no required dependencies.
- Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The PBRF package has no suggested dependencies.
- External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The PBRF 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 PBRF package has no imported packages.
- Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The PBRF package has no enhancements.
How to get help with the PBRF package?
In this section, we’ll discuss a variety of available resources for getting help with the PBRF package.
Key resources
- The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("PBRF") function to retrieve detailed information, examples, and usage instructions. Alternatively, you can use the ? operator as a shortcut.
-
Package website: The PBRF package does not have a dedicated website.
- Developer support: You can email Xiaodong Luo . For contact information, visit our R community directory.
Additional resources
- AI tools: Use AI programming tools like DataLab to promptly address your questions. (Please note that we may earn a commission if you purchase through this link. By trying DataLab, you help sustain our platform. Thank you for your support!)
- Online courses: Try our handpicked collection of R programming courses designed to boost your proficiency in R programming.
- Books: Explore our curated selection of R programming books tailored to help you master R programming.
- Discussion forums: Online forums are excellent platforms to ask questions, share knowledge, and troubleshoot issues. The most popular forums for R programmers are StackOverflow and Posit Community.
Other package guides
Explore our comprehensive guides for other R packages. These guides are valuable resources for accessing a wide range of information, making it easier to navigate R documentation in one place.
Share the Post: