From 8e588ce5eff56e334ad2cffce90f6affba599819 Mon Sep 17 00:00:00 2001 From: Logan Bhamidipaty Date: Wed, 11 Dec 2024 21:29:26 -0800 Subject: [PATCH] added codecov badge --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 4c83359..5ed4c3e 100644 --- a/README.md +++ b/README.md @@ -3,6 +3,7 @@ [![Build Status](https://github.com/sisl/ExpFamilyPCA.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/sisl/ExpFamilyPCA.jl/actions/workflows/CI.yml?query=branch%3Amain) [![Dev-Docs](https://img.shields.io/badge/docs-latest-blue.svg)](https://sisl.github.io/ExpFamilyPCA.jl/dev/) [![status](https://joss.theoj.org/papers/8c617a932d19b28d5ac0299b23d2c8dc/status.svg)](https://joss.theoj.org/papers/8c617a932d19b28d5ac0299b23d2c8dc) +[![codecov](https://codecov.io/github/sisl/ExpFamilyPCA.jl/graph/badge.svg?token=kJESb0GybB)](https://codecov.io/github/sisl/ExpFamilyPCA.jl) **ExpFamilyPCA.jl** is a Julia package for [exponential family principal component analysis (EPCA)](https://papers.nips.cc/paper_files/paper/2001/hash/f410588e48dc83f2822a880a68f78923-Abstract.html), a versatile generalization of PCA designed to handle non-Gaussian data, enabling dimensionality reduction and data analysis across a wide variety of distributions (e.g., binary, count, and compositional data). It is designed for applications in machine learning (belief compression, text analysis), signal processing (denoising), and data science (sample debiasing, clustering, dimensionality reduction), but can be applied to other fields with diverse data types.