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Copy file name to clipboardExpand all lines: CONTRIBUTING.md
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This Git repository uses the [Git Flow](https://nvie.com/posts/a-successful-git-branching-model/) branching model (the [`git flow`](https://github.com/petervanderdoes/gitflow-avh) extension is useful for this). The [`develop`](https://github.com/HenrikBengtsson/progressr/tree/develop) branch contains the latest contributions and other code that will appear in the next release, and the [`master`](https://github.com/HenrikBengtsson/progressr) branch contains the code of the latest release, which is exactly what is currently on [CRAN](https://cran.r-project.org/package=progressr).
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Contributing to this package is easy. Just send a [pull request](https://help.github.com/articles/using-pull-requests/). When you send your PR, make sure `develop` is the destination branch on the [progressr repository](https://github.com/HenrikBengtsson/progressr). Your PR should pass `R CMD check --as-cran`, which will also be checked by <ahref="https://github.com/HenrikBengtsson/progressr/actions?query=workflow%3AR-CMD-check">GitHub Actions</a>, <ahref="https://travis-ci.org/HenrikBengtsson/progressr">Travis CI</a>, and <ahref="https://ci.appveyor.com/project/HenrikBengtsson/progressr">AppVeyor CI</a> when the PR is submitted.
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Contributing to this package is easy. Just send a [pull request](https://help.github.com/articles/using-pull-requests/). When you send your PR, make sure `develop` is the destination branch on the [progressr repository](https://github.com/HenrikBengtsson/progressr). Your PR should pass `R CMD check --as-cran`, which will also be checked by <ahref="https://github.com/HenrikBengtsson/progressr/actions?query=workflow%3AR-CMD-check">GitHub Actions</a>and when the PR is submitted.
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We abide to the [Code of Conduct](https://www.contributor-covenant.org/version/2/0/code_of_conduct/) of Contributor Covenant.
Copy file name to clipboardExpand all lines: DESCRIPTION
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Package: progressr
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Version: 0.8.0
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Version: 0.9.0
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Title: An Inclusive, Unifying API for Progress Updates
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Description: A minimal, unifying API for scripts and packages to report progress updates from anywhere including when using parallel processing. The package is designed such that the developer can to focus on what progress should be reported on without having to worry about how to present it. The end user has full control of how, where, and when to render these progress updates, e.g. in the terminal using utils::txtProgressBar() or progress::progress_bar(), in a graphical user interface using utils::winProgressBar(), tcltk::tkProgressBar() or shiny::withProgress(), via the speakers using beep::beepr(), or on a file system via the size of a file. Anyone can add additional, customized, progression handlers. The 'progressr' package uses R's condition framework for signaling progress updated. Because of this, progress can be reported from almost anywhere in R, e.g. from classical for and while loops, from map-reduce APIs like the lapply() family of functions, 'purrr', 'plyr', and 'foreach'. It will also work with parallel processing via the 'future' framework, e.g. future.apply::future_lapply(), furrr::future_map(), and 'foreach' with 'doFuture'. The package is compatible with Shiny applications.
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Description: A minimal, unifying API for scripts and packages to report progress updates from anywhere including when using parallel processing. The package is designed such that the developer can to focus on what progress should be reported on without having to worry about how to present it. The end user has full control of how, where, and when to render these progress updates, e.g. in the terminal using utils::txtProgressBar() or progress::progress_bar(), in a graphical user interface using utils::winProgressBar(), tcltk::tkProgressBar() or shiny::withProgress(), via the speakers using beep::beepr(), or on a file system via the size of a file. Anyone can add additional, customized, progression handlers. The 'progressr' package uses R's condition framework for signaling progress updated. Because of this, progress can be reported from almost anywhere in R, e.g. from classical for and while loops, from map-reduce API:s like the lapply() family of functions, 'purrr', 'plyr', and 'foreach'. It will also work with parallel processing via the 'future' framework, e.g. future.apply::future_lapply(), furrr::future_map(), and 'foreach' with 'doFuture'. The package is compatible with Shiny applications.
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