README.md

In eifer4/CoarsePosteriorSummary: Interpretable Model Summaries Using the Wasserstein Distance

Interpretable Model Summaries Using the Wasserstein Distance

Replicated the code for Dunipace, E., & Trippa, L. (2020). Interpretable Model Summaries Using the Wasserstein Distance. (2018), 1–42. Retrieved from http://arxiv.org/abs/2012.09999

Liability

THIS SOURCE CODE IS SUPPLIED “AS IS” WITHOUT WARRANTY OF ANY KIND, AND ITS AUTHOR AND THE JOURNAL OF MACHINE LEARNING RESEARCH (JMLR) AND JMLR’S PUBLISHERS AND DISTRIBUTORS, DISCLAIM ANY AND ALL WARRANTIES, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, AND ANY WARRANTIES OR NON INFRINGEMENT. THE USER ASSUMES ALL LIABILITY AND RESPONSIBILITY FOR USE OF THIS SOURCE CODE, AND NEITHER THE AUTHOR NOR JMLR, NOR JMLR’S PUBLISHERS AND DISTRIBUTORS, WILL BE LIABLE FOR DAMAGES OF ANY KIND RESULTING FROM ITS USE. Without limiting the generality of the foregoing, neither the author, nor JMLR, nor JMLR’s publishers and distributors, warrant that the Source Code will be error-free, will operate without interruption, or will meet the needs of the user.

Installation

To install the code you can do one of the following

1. Install directly from Github

  devtools::install_github("ericdunipace/SLIMpaper")

2. Or clone the repository and then install with devtools

You can download the repository as a zip file or clone directly from the terminal and then install the repository using devtools.

devtools::install("SLIMpaper")

Either of these options should also install the workhorse package found at http://www.github.com/ericdunipace/WpProj

eifer4/CoarsePosteriorSummary documentation built on April 10, 2021, 12:40 p.m.