The Ultimate Guide to the kantorovich Package in R
Explore valuable documentation and insights to make the most of the kantorovich package in R. Get ready to unlock the full potential of the kantorovich package!
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What is the kantorovich package?
In this section, we’ll delve into the fundamental aspects and key features of the package.
The kantorovich package implements the Kantorovich monad, a mathematical construct used in theoretical computer science and category theory. It enables users to work with composable computations and can be used in a wide range of tasks, from data transformation to machine learning.
- Title: Kantorovich Distance Between Probability Measures
- Description: Computes the Kantorovich distance between two probability measures on a finite set. The Kantorovich distance is also known as the Monge-Kantorovich distance or the first Wasserstein distance.
- Author: Stéphane Laurent
- Maintainer: Stéphane Laurent
How to install the kantorovich package?
In this section, we’ll walk you through the process of installing and loading the kantorovich 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 kantorovich 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 kantorovich 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 kantorovich 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 kantorovich 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 kantorovich 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 kantorovich package has the following required dependencies: R (>= 4.0.0).
- Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The kantorovich package has the following suggested dependencies: knitr, rmarkdown, testthat (>= 3.0.0).
- External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The kantorovich 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 kantorovich package has the following imported packages: CVXR, gmp, lpSolve, methods, rcdd, Rglpk, slam, utils, ompr, ompr.roi, ROI.plugin.glpk.
- Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The kantorovich package has no enhancements.
How to get help with the kantorovich package?
In this section, we’ll discuss a variety of available resources for getting help with the kantorovich package.
Key resources
- The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("kantorovich") 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 kantorovich package has a dedicated website. You can visit: https://github.com/stla/kantorovich.
- Developer support: You can email Stéphane Laurent . For contact information, visit our R community directory.
Additional resources
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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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