From b5feafa72253d9c658d01f0b21a07600839d1fff Mon Sep 17 00:00:00 2001
From: fradenti <francescodenti.personal@gmail.com>
Date: Fri, 6 Oct 2023 10:46:17 +0200
Subject: [PATCH] more readme

---
 README.Rmd | 16 +++++++---------
 README.md  | 31 ++++++++++++++-----------------
 2 files changed, 21 insertions(+), 26 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index 0c5428f..84a34b2 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -21,14 +21,6 @@ knitr::opts_chunk$set(
 
 The goal of SANple is to estimate Bayesian nested mixture models via MCMC methods. Specifically, the package implements the common atoms model (Denti et al., 2023), its finite version (D'Angelo et al., 2023), and a hybrid finite-infinite model (D'Angelo and Denti, 2023+). All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyzing the results of the fitting procedure.
 
-### References
-D’Angelo, L., Canale, A., Yu, Z., Guindani, M. (2023). Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data. *Biometrics* 79(2), 1370--1382. doi:10.1111/biom.13626.
-
-D’Angelo, L., and Denti, F. (2023+). A finite-infinite shared atoms nested model for the Bayesian analysis of large grouped data sets. *Working paper* 0--23.
-
-Denti, F., Camerlenghi, F., Guindani, M., Mira, A., (2023). A Common Atoms Model for the Bayesian Nonparametric Analysis of Nested Data. *Journal of the American Statistical Association*. 118(541), 405--416. doi:10.1080/01621459.2021.1933499.
-
-
 ## Installation
 
 You can install the development version of SANple from [GitHub](https://github.com/) with:
@@ -58,4 +50,10 @@ clusters
 plot(out, estimated_clusters = clusters)
 ```
 
-...
\ No newline at end of file
+# References
+
+D’Angelo, L., Canale, A., Yu, Z., Guindani, M. (2023). Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data. *Biometrics* 79(2), 1370–1382. \doi{10.1111/biom.13626}
+
+D’Angelo, L., and Denti, F. (2023+). A finite-infinite shared atoms nested model for the Bayesian analysis of large grouped data sets. *Working paper*, 1–23.
+
+Denti, F., Camerlenghi, F., Guindani, M., Mira, A., 2023. A Common Atoms Model for the Bayesian Nonparametric Analysis of Nested Data. *Journal of the American Statistical Association*. 118(541), 405–416. \doi{10.1080/01621459.2021.1933499}
diff --git a/README.md b/README.md
index 5bfe5d6..408b01d 100644
--- a/README.md
+++ b/README.md
@@ -16,21 +16,6 @@ models use Gaussian mixtures with a normal-inverse-gamma prior
 distribution on the parameters. Additional functions are provided to
 help analyzing the results of the fitting procedure.
 
-### References
-
-D’Angelo, L., Canale, A., Yu, Z., Guindani, M. (2023). Bayesian
-nonparametric analysis for the detection of spikes in noisy calcium
-imaging data. *Biometrics* 79(2), 1370–1382. <doi:10.1111/biom.13626>.
-
-D’Angelo, L., and Denti, F. (2023+). A finite-infinite shared atoms
-nested model for the Bayesian analysis of large grouped data sets.
-*Working paper* 0–23.
-
-Denti, F., Camerlenghi, F., Guindani, M., Mira, A., (2023). A Common
-Atoms Model for the Bayesian Nonparametric Analysis of Nested Data.
-*Journal of the American Statistical Association*. 118(541), 405–416.
-<doi:10.1080/01621459.2021.1933499>.
-
 ## Installation
 
 You can install the development version of SANple from
@@ -72,7 +57,7 @@ out
 #> Model estimated on 290 total observations and 2 groups 
 #> Total MCMC iterations: 3000 
 #> maxL: 50 - maxK: 50 
-#> Elapsed time: 1.583 secs
+#> Elapsed time: 1.585 secs
 clusters <- estimate_clusters(out, burnin = 2000)
 clusters
 #> 
@@ -97,4 +82,16 @@ plot(out, estimated_clusters = clusters)
 
 <img src="man/figures/README-example-2.png" width="100%" />
 
-…
+# References
+
+D’Angelo, L., Canale, A., Yu, Z., Guindani, M. (2023). Bayesian
+nonparametric analysis for the detection of spikes in noisy calcium
+imaging data. *Biometrics* 79(2), 1370–1382.
+
+D’Angelo, L., and Denti, F. (2023+). A finite-infinite shared atoms
+nested model for the Bayesian analysis of large grouped data sets.
+*Working paper*, 1–23.
+
+Denti, F., Camerlenghi, F., Guindani, M., Mira, A., 2023. A Common Atoms
+Model for the Bayesian Nonparametric Analysis of Nested Data. *Journal
+of the American Statistical Association*. 118(541), 405–416.