From: Benjamin Auder <benjamin.auder@somewhere>
Date: Thu, 23 Mar 2017 14:46:29 +0000 (+0100)
Subject: downgrade roxygen2 to v5.0.1, rename EMGLLF -> EMGLLF_R
X-Git-Url: https://git.auder.net/img/css/current/doc/screen_ranking.png?a=commitdiff_plain;h=f9143bd90ee989e7fede640b4c411374be8e2099;p=valse.git

downgrade roxygen2 to v5.0.1, rename EMGLLF -> EMGLLF_R
---

diff --git a/pkg/DESCRIPTION b/pkg/DESCRIPTION
index d3841a7..5febdc0 100644
--- a/pkg/DESCRIPTION
+++ b/pkg/DESCRIPTION
@@ -2,15 +2,15 @@ Package: valse
 Title: Variable Selection With Mixture Of Models
 Date: 2016-12-01
 Version: 0.1-0
-Description: Two methods are implemented to cluster data with finite mixture regression models.
-    Those procedures deal with high-dimensional covariates and responses through a variable
-    selection procedure based on the Lasso estimator. A low-rank constraint could be added,
-    computed for the Lasso-Rank procedure.
-    A collection of models is constructed, varying the level of sparsity and the number of
-    clusters, and a model is selected using a model selection criterion (slope heuristic,
-    BIC or AIC). Details of the procedure are provided in 'Model-based clustering for
-    high-dimensional data. Application to functional data' by Emilie Devijver, published in
-    Advances in Data Analysis and Clustering (2016).
+Description: Two methods are implemented to cluster data with finite mixture
+    regression models. Those procedures deal with high-dimensional covariates and
+    responses through a variable selection procedure based on the Lasso estimator.
+    A low-rank constraint could be added, computed for the Lasso-Rank procedure.
+    A collection of models is constructed, varying the level of sparsity and the
+    number of clusters, and a model is selected using a model selection criterion
+    (slope heuristic, BIC or AIC). Details of the procedure are provided in 'Model-
+    based clustering for high-dimensional data. Application to functional data' by
+    Emilie Devijver, published in Advances in Data Analysis and Clustering (2016).
 Author: Benjamin Auder <Benjamin.Auder@math.u-psud.fr> [aut,cre],
     Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut],
     Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [aut]
@@ -28,4 +28,4 @@ Suggests:
 URL: http://git.auder.net/?p=valse.git
 License: MIT + file LICENSE
 VignetteBuilder: knitr
-RoxygenNote: 6.0.1
+RoxygenNote: 5.0.1
diff --git a/test/generate_test_data/EMGLLF.R b/test/generate_test_data/EMGLLF.R
index de6d40e..37859d8 100644
--- a/test/generate_test_data/EMGLLF.R
+++ b/test/generate_test_data/EMGLLF.R
@@ -1,4 +1,4 @@
-EMGLLF = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau)
+EMGLLF_R = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau)
 {
   #matrix dimensions
   n = dim(X)[1]