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Please edit that file --\u003e\n\n```{r, echo = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\"\n)\n```\n\n# dabestr \u003cimg src=\"man/figures/logo.png\" align=\"right\" height=\"139\" /\u003e\u003c/a\u003e\n\n\u003c!-- badges: start --\u003e\n[![minimal R version](https://img.shields.io/badge/R%3E%3D-2.10-6666ff.svg)](https://cran.r-project.org/) [![CRAN Download Count](https://cranlogs.r-pkg.org/badges/grand-total/dabestr?color=brightgreen)](https://cran.r-project.org/package=dabestr) [![Free-to-view citation](https://zenodo.org/badge/DOI/10.1038/s41592-019-0470-3.svg)](https://www.nature.com/articles/s41592-019-0470-3.epdf?author_access_token=Euy6APITxsYA3huBKOFBvNRgN0jAjWel9jnR3ZoTv0Pr6zJiJ3AA5aH4989gOJS_dajtNr1Wt17D0fh-t4GFcvqwMYN03qb8C33na_UrCUcGrt-Z0J9aPL6TPSbOxIC-pbHWKUDo2XsUOr3hQmlRew%3D%3D) [![License](https://img.shields.io/badge/License-Apache_2.0-orange.svg)](https://spdx.org/licenses/BSD-3-Clause-Clear.html)\n[![R-CMD-check](https://github.com/sunroofgod/dabestr-prototype/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/sunroofgod/dabestr-prototype/actions/workflows/R-CMD-check.yaml)\n\u003c!-- badges: end --\u003e\n\n\u003c!-- ## Overview --\u003e\ndabestr is a package for **D**ata **A**nalysis using **B**ootstrap-Coupled **EST**imation.\n\n[Estimation statistics](https://en.wikipedia.org/wiki/Estimation_statistics \"Estimation Stats on Wikipedia\") is a [simple framework](https://thenewstatistics.com/itns/ \"Introduction to the New Statistics\") that avoids the [pitfalls](https://www.nature.com/articles/nmeth.3288 \"The fickle P value generates irreproducible results, Halsey et al 2015\") of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by *P* values.\n\nAn estimation plot has two key features.\n\n1.  It **presents all datapoints** as a swarmplot, which orders each point to display the underlying distribution.\n\n2.  It presents the **effect size** as a **bootstrap 95% confidence interval** on a **separate but aligned axes**.\n\nThe `dabestr` package powers [estimationstats.com](http://estimationstats.com), allowing everyone access to high-quality estimation plots.\n\n## Installation\n\n```{r, eval = FALSE}\n# Install it from CRAN\ninstall.packages(\"dabestr\")\n\n# Or the development version from GitHub:\n# install.packages(\"devtools\")\ndevtools::install_github(repo = \"ACCLAB/dabestr\", ref = \"dev\")\n```\n\n## Usage\n\n```{r, warning = FALSE, message = FALSE, eval = FALSE}\nlibrary(dabestr)\n```\n\n```{r, include = FALSE}\ndevtools::load_all(\".\")\n```\n\n```{r, dpi = 500, warning = FALSE}\ndata(\"non_proportional_data\")\n\ndabest_obj.mean_diff \u003c- load(\n  data = non_proportional_data,\n  x = Group,\n  y = Measurement,\n  idx = c(\"Control 1\", \"Test 1\")\n) %\u003e%\n  mean_diff()\n\ndabest_plot(dabest_obj.mean_diff, TRUE)\n```\n\nPlease refer to the official [tutorial](https://acclab.github.io/dabestr/articles/tutorial_basics.html) for more useful code snippets.\n\n## Citation \n\n**Moving beyond P values: Everyday data analysis with estimation plots**\n\n*Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang*\n\nNature Methods 2019, 1548-7105. [10.1038/s41592-019-0470-3](http://dx.doi.org/10.1038/s41592-019-0470-3)\n\n[Paywalled publisher site](https://www.nature.com/articles/s41592-019-0470-3); [Free-to-view PDF](https://www.nature.com/articles/s41592-019-0470-3.epdf?author_access_token=Euy6APITxsYA3huBKOFBvNRgN0jAjWel9jnR3ZoTv0Pr6zJiJ3AA5aH4989gOJS_dajtNr1Wt17D0fh-t4GFcvqwMYN03qb8C33na_UrCUcGrt-Z0J9aPL6TPSbOxIC-pbHWKUDo2XsUOr3hQmlRew%3D%3D)\n\n## Contributing\n\nPlease report any bugs on the [Github issue tracker](https://github.com/ACCLAB/dabestr/issues/new).\n\nAll contributions are welcome; please read the [Guidelines for contributing](https://github.com/ACCLAB/dabestr/blob/master/CONTRIBUTING.md) first.\n\nWe also have a [Code of Conduct](https://github.com/ACCLAB/dabestr/blob/master/CODE_OF_CONDUCT.md) to foster an inclusive and productive space.\n\n## Acknowledgements\n\nWe would like to thank alpha testers from the [Claridge-Chang lab](https://www.claridgechang.net/): [Sangyu Xu](https://github.com/sangyu), [Xianyuan Zhang](https://github.com/XYZfar), [Farhan Mohammad](https://github.com/farhan8igib), Jurga Mituzaitė, and Stanislav Ott.\n\n## DABEST in other languages\n\nDABEST is also available in Python ([DABEST-python](https://github.com/ACCLAB/DABEST-python \"DABEST-Python on Github\")) and Matlab\n([DABEST-Matlab](https://github.com/ACCLAB/DABEST-Matlab \"DABEST-Matlab on Github\")).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Facclab%2Fdabestr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Facclab%2Fdabestr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Facclab%2Fdabestr/lists"}