add CRAN suggestions - citation + ncores==2

DESCRIPTION

@@ -17,7 +17,8 @@ Description: A method for detecting outliers with a Kalman filter on impulsed
   are data points that differ significantly from other observations. 'ML' 
   (Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm) 
   algorithms were implemented in 'kfino'. The method is described in full 
-  details in the following arXiv e-Print: <arXiv:2208.00961>.
+  details in the following e-Print by Cloez B., Fontez B., González García E. 
+  and Sanchez I. (2022) <arXiv:2208.00961>.
 License: GPL-3
 Depends: R (>= 4.1.0)
 Encoding: UTF-8

doc/HowTo.html

@@ -1512,7 +1512,7 @@ div.tocify {
 <h1 class="title toc-ignore">How to perform a kfino outlier
 detection</h1>
 <h4 class="author">B. Cloez &amp; I. Sanchez</h4>
-<h4 class="date">octobre 05, 2022</h4>
+<h4 class="date">octobre 10, 2022</h4>
 </div>

doc/multipleFit.R

@@ -75,8 +75,12 @@ simpleCall<-function(datain,Index,Tvar,Yvar,param){
   return(tp.resu)
 }
 
-ncores<-parallel::detectCores()
-myCluster<-parallel::makeCluster(ncores - 1)
+# In a real example, take advantage of as many cores as you can
+# ncores<-parallel::detectCores()
+# myCluster<-parallel::makeCluster(ncores - 1)
+
+# For CRAN requirements, we use here only 2 cores.
+myCluster <- parallel::makeCluster(2)
 doParallel::registerDoParallel(myCluster)
 
 resu2<-foreach(i=seq_along(myIDE), .packages="kfino") %dopar% 

doc/multipleFit.Rmd

@@ -105,8 +105,12 @@ simpleCall<-function(datain,Index,Tvar,Yvar,param){
   return(tp.resu)
 }
 
-ncores<-parallel::detectCores()
-myCluster<-parallel::makeCluster(ncores - 1)
+# In a real example, take advantage of as many cores as you can
+# ncores<-parallel::detectCores()
+# myCluster<-parallel::makeCluster(ncores - 1)
+
+# For CRAN requirements, we use here only 2 cores.
+myCluster <- parallel::makeCluster(2)
 doParallel::registerDoParallel(myCluster)
 
 resu2<-foreach(i=seq_along(myIDE), .packages="kfino") %dopar% 

doc/multipleFit.html

@@ -1512,7 +1512,7 @@ div.tocify {
 <h1 class="title toc-ignore">How to perform a kfino outlier detection on
 multiple individuals</h1>
 <h4 class="author">B. Cloez &amp; I. Sanchez</h4>
-<h4 class="date">octobre 05, 2022</h4>
+<h4 class="date">octobre 10, 2022</h4>
 </div>
@@ -1571,7 +1571,7 @@ for (i in seq_along(myIDE)){
 #&gt; [1] &quot;250017033503096&quot;
 #&gt; [1] 566   5
 Sys.time() - t0
-#&gt; Time difference of 18.57455 secs
+#&gt; Time difference of 18.28121 secs
 print(length(resu1))
 #&gt; [1] 4</code></pre>
@@ -1607,8 +1607,12 @@ simpleCall&lt;-function(datain,Index,Tvar,Yvar,param){
   return(tp.resu)
 }
-ncores&lt;-parallel::detectCores()
-myCluster&lt;-parallel::makeCluster(ncores - 1)
+# In a real example, take advantage of as many cores as you can
+# ncores&lt;-parallel::detectCores()
+# myCluster&lt;-parallel::makeCluster(ncores - 1)
+
+# For CRAN requirements, we use here only 2 cores.
+myCluster &lt;- parallel::makeCluster(2)
 doParallel::registerDoParallel(myCluster)
 resu2&lt;-foreach(i=seq_along(myIDE), .packages=&quot;kfino&quot;) %dopar% 
@@ -1620,7 +1624,7 @@ resu2&lt;-foreach(i=seq_along(myIDE), .packages=&quot;kfino&quot;) %dopar%
 parallel::stopCluster(myCluster)
 Sys.time() - t0
-#&gt; Time difference of 9.251507 secs
+#&gt; Time difference of 12.13639 secs
 print(length(resu2))
 #&gt; [1] 4</code></pre>

man/kfino.Rd

@@ -8,7 +8,7 @@
 \description{
 \if{html}{\figure{logo.png}{options: style='float: right' alt='logo' width='120'}}
 
-A method for detecting outliers with a Kalman filter on impulsed noised outliers and prediction on cleaned data. 'kfino' is a robust sequential algorithm allowing to filter data with a large number of outliers. This algorithm is based on simple latent linear Gaussian processes as in the Kalman Filter method and is devoted to detect impulse-noised outliers. These are data points that differ significantly from other observations. 'ML' (Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm) algorithms were implemented in 'kfino'. The method is described in full details in the following arXiv e-Print: \href{https://arxiv.org/abs/2208.00961}{arXiv:2208.00961}.
+A method for detecting outliers with a Kalman filter on impulsed noised outliers and prediction on cleaned data. 'kfino' is a robust sequential algorithm allowing to filter data with a large number of outliers. This algorithm is based on simple latent linear Gaussian processes as in the Kalman Filter method and is devoted to detect impulse-noised outliers. These are data points that differ significantly from other observations. 'ML' (Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm) algorithms were implemented in 'kfino'. The method is described in full details in the following e-Print by Cloez B., Fontez B., González García E. and Sanchez I. (2022) \href{https://arxiv.org/abs/2208.00961}{arXiv:2208.00961}.
 }
 \details{
 xxxxxxxx xxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx.

vignettes/multipleFit.Rmd

@@ -105,8 +105,12 @@ simpleCall<-function(datain,Index,Tvar,Yvar,param){
   return(tp.resu)
 }
 
-ncores<-parallel::detectCores()
-myCluster<-parallel::makeCluster(ncores - 1)
+# In a real example, take advantage of as many cores as you can
+# ncores<-parallel::detectCores()
+# myCluster<-parallel::makeCluster(ncores - 1)
+
+# For CRAN requirements, we use here only 2 cores.
+myCluster <- parallel::makeCluster(2)
 doParallel::registerDoParallel(myCluster)
 
 resu2<-foreach(i=seq_along(myIDE), .packages="kfino") %dopar%