The Ultimate Guide to the occ Package in R
Explore valuable documentation and insights to make the most of the occ package in R. Get ready to unlock the full potential of the occ package!
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What is the occ package?
In this section, we’ll delve into the fundamental aspects and key features of the package.
The occ package offers tools for occurrence data handling in R. It is especially useful for handling large occurrence data sets, such as those used in ecology and biogeography, providing functionalities for quality control, filtering and analysis.
- Title: Estimates PET Neuroreceptor Occupancies
- Description: Generic function for estimating positron emission tomography (PET) neuroreceptor occupancies from the total volumes of distribution of a set of regions of interest. Fittings methods include the simple 'reference region' and 'ordinary least squares' (sometimes known as occupancy plot) methods, as well as the more efficient 'restricted maximum likelihood estimation'.
- Author: Joaquim Radua
- Maintainer: Joaquim Radua
How to install the occ package?
In this section, we’ll walk you through the process of installing and loading the occ package. By following these steps, you can seamlessly add new functions, datasets, and other resources to your R environment for a more robust workflow.
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What package information should you know?
In this section, we’ll go over the technical aspects of the occ 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 occ 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 occ 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 occ package, visit our database of R package citations.
Technical details
- License type: GPL-3. 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 occ package does not have compilation requirements.
- Required dependencies: A required dependency refers to another package that is essential for the functioning of the main package. The occ 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 occ package has no suggested dependencies.
- External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The occ 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 occ package has no imported packages.
- Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The occ package has no enhancements.
How to get help with the occ package?
In this section, we’ll discuss a variety of available resources for getting help with the occ package.
Key resources
- The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("occ") function to retrieve detailed information, examples, and usage instructions. Alternatively, you can use the ? operator as a shortcut.
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Package website: The occ package does not have a dedicated website.
- Developer support: You can email Joaquim Radua . For contact information, visit our R community directory.
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- 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.
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