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YangLabHKUST · May 18, 2023 · 7ae78c7 · 7ae78c7
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diff --git a/README.Rmd b/README.Rmd
@@ -20,7 +20,7 @@ knitr::opts_chunk$set(
 [![R-CMD-check](https://github.com/YangLabHKUST/mfair/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/YangLabHKUST/mfair/actions/workflows/R-CMD-check.yaml)
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-Matrix factorization methods based on the paper [MFAI: A scalable Bayesian matrix factorization approach to leveraging auxiliary information](https://doi.org/10.48550/arXiv.2303.02566).
+The R package `mfair` implements the methods based on the paper [MFAI: A scalable Bayesian matrix factorization approach to leveraging auxiliary information](https://doi.org/10.48550/arXiv.2303.02566).
 MFAI integrates gradient boosted trees in the probabilistic matrix factorization framework to effectively leverage auxiliary information.
 The parameters in MAFI can be automatically determined under the empirical Bayes framework, making it adaptive to the utilization of auxiliary information and immune to overfitting.
 

diff --git a/README.md b/README.md
@@ -10,8 +10,8 @@ coverage](https://codecov.io/gh/YangLabHKUST/mfair/branch/main/graph/badge.svg)]
 [![R-CMD-check](https://github.com/YangLabHKUST/mfair/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/YangLabHKUST/mfair/actions/workflows/R-CMD-check.yaml)
 <!-- badges: end -->
 
-Matrix factorization methods based on the paper [MFAI: A scalable
-Bayesian matrix factorization approach to leveraging auxiliary
+The R package `mfair` implements the methods based on the paper [MFAI: A
+scalable Bayesian matrix factorization approach to leveraging auxiliary
 information](https://doi.org/10.48550/arXiv.2303.02566). MFAI integrates
 gradient boosted trees in the probabilistic matrix factorization
 framework to effectively leverage auxiliary information. The parameters