diff --git a/DESCRIPTION b/DESCRIPTION
index becdb7c..ed4c688 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: pcalg
-Version: 2.6-10
-Date: 2020-03-3
+Version: 2.6-11
+Date: 2020-08-12
 Title: Methods for Graphical Models and Causal Inference
 Description: Functions for causal structure
   learning and causal inference using graphical models. The main algorithms
@@ -51,6 +51,6 @@ Encoding: UTF-8
 License: GPL (>= 2)
 URL: http://pcalg.r-forge.r-project.org/
 RoxygenNote: 6.1.1
-Packaged: 2020-03-03 12:31:36 UTC; kalischm
+Packaged: 2020-08-12 12:36:11 UTC; kalischm
 Repository: CRAN
-Date/Publication: 2020-03-03 14:40:07 UTC
+Date/Publication: 2020-08-16 16:00:02 UTC
diff --git a/MD5 b/MD5
index 562b4bd..352db29 100644
--- a/MD5
+++ b/MD5
@@ -1,6 +1,6 @@
 1c213fc1c5a5ec68e7a5ba9293440dc3 *ChangeLog
-4b9b32644c238bb95ec960bbfc0bff0a *DESCRIPTION
-f813efd2d2208b900ae1d5ceb8dce2cf *NAMESPACE
+ae6a1555639ce7d21db187bdd7482215 *DESCRIPTION
+f9c1992369a6565cdfb3fdf5e8d8ebd1 *NAMESPACE
 66963591a11cfdb002f5da9070f1500b *R/Aaux.R
 83c288d2212114e54bfe624e0edecaaa *R/AllClasses.R
 9f99e28f01bd00dedf565c6e01978676 *R/adjustment.R
@@ -11,14 +11,14 @@ f813efd2d2208b900ae1d5ceb8dce2cf *NAMESPACE
 5c33d7a0849fd620631eb901d6a6540d *R/gies.R
 aca17dcd6f37ce1136086a8bc285046e *R/isValidGraph.R
 b363657f7e2c52dc0f2aa083780fa0f7 *R/jointIda.R
-386ed9be1a524de90dfac185433b009a *R/lingamFuns.R
+f28cf0b020d974238796d27cfde61bf4 *R/lingamFuns.R
 ace998420ba6145fe7fe3c75c5892e66 *R/optAdjSet.R
-af4239ca324cb48eaac6b07fc0cf0061 *R/pcalg.R
+37f26524d577b5e73c98060c9eb6a057 *R/pcalg.R
 67ec62ad56d7cffaba4bee8f88797136 *R/pcalg2dagitty.R
 f84d95dd282c42c32595a27ca98f9a3d *R/possDeAn.R
 0db1ba6d57801a4d8fc6cdf996384a4d *R/zzz.R
 d599e23940bca0203504c0fa6b09e01c *TODO
-94bed761e02266860aff243b04717fed *build/vignette.rds
+f0b3f7d4b26e8dde3ea4a4a4d690f0f9 *build/vignette.rds
 f832779eae511757a99906c7670fc0ad *cleanup
 7cbe431a233e6c454e24f7b92d63fe75 *data/datalist
 1d8d4c43b9cbb9dc9dd57acd59f565bf *data/gmB.rda
@@ -27,15 +27,15 @@ f832779eae511757a99906c7670fc0ad *cleanup
 6d96fac64134a91ecf0c244b4679ec0f *data/gmI.rda
 385d41164301819ce274673db8743fc9 *data/gmInt.rda
 1b3a78b432a5e46b991f5ae5eb4b77f3 *data/gmL.rda
-811a9708268fbddd317bbca12ef93a55 *inst/CITATION
+7207fa41fa9372e94d6799af40bd4aa1 *inst/CITATION
 2f13a20f69511d72dd63f9212308bc80 *inst/NEWS.Rd
 4e33425acbef8bf36705d8a75622baa8 *inst/doc/mkVignettes.R
-cc46b1f49edd31faf5316d5cf4d6c1ce *inst/doc/pcalgDoc.R
+ea230ec4a19260aa9a751cef43965296 *inst/doc/pcalgDoc.R
 6581d4416f174d4d6f07a2a2d4b9d3cb *inst/doc/pcalgDoc.Rnw
-277b633e6b422057891d5e6722d84add *inst/doc/pcalgDoc.pdf
-01c5a9cb2024f1234d7c4aebb9762db5 *inst/doc/vignette2018.R
-65ba145dfc8ae12f50c95d77a8bf8600 *inst/doc/vignette2018.Rnw
-23165d2276ae3dd8fee95d261b9b2da9 *inst/doc/vignette2018.pdf
+14ab89a1cd46bbaaa76ee71af0f84bc4 *inst/doc/pcalgDoc.pdf
+c7e3ec8f056bcb4ffedf8f38e06e1692 *inst/doc/vignette2018.R
+e2e81e4c1014989c895e5764846e7a3e *inst/doc/vignette2018.Rnw
+e4cb288353e02f194bd8a1a984ca0192 *inst/doc/vignette2018.pdf
 cee0b4475c720bf58c466e1495451b22 *inst/external/N_6_1000.rds
 0d84fdc119d76d992b72f9bdbd0e613d *inst/external/gac-pags.rds
 e7a2a9117d97986b2ab6afc6b4f77478 *inst/external/test_conservative_pc_data1.rda
@@ -72,7 +72,7 @@ b2ded6b2c0cd2cf0c30b845d07646736 *man/corGraph.Rd
 3d880e7f58fe68731e9ed4ecb4c13bf4 *man/dreach.Rd
 e89cc00623c136f141b0427a7476889a *man/dsep.Rd
 3c0924e854ffc20b168281b2380d9b61 *man/dsepTest.Rd
-e3ec67ee2b013c1127ca799376a2a115 *man/fci.Rd
+87ca8597438b85a5d9f801114ec715e4 *man/fci.Rd
 4210c617d4929290e295ced5e0eeeb43 *man/fciAlgo-class.Rd
 7fb4c8797b23d3d73252beee2610d45f *man/fciPlus.Rd
 44127d2a6a6234ed84cf650a3273e504 *man/find.unsh.triple.Rd
@@ -89,7 +89,7 @@ cbb33c4976caf365b69cb9c76a7a493e *man/gmB.Rd
 66a7855809f1fd6e9a0ced7dbd612af6 *man/gmI.Rd
 6a624691a6a19dd55526a9e9585ce0b3 *man/gmInt.Rd
 0fc0725cd27113dfa0505deb9504b3ec *man/gmL.Rd
-782a90276af56e6801b9edddc2794197 *man/ida.Rd
+eb1f2a346b3a92262713be8090f9e64d *man/ida.Rd
 7aa46e96c96d02d55e876f3d52c2d5d7 *man/idaFast.Rd
 90a088350b43454d621b4b315ab3d2cf *man/iplotPC.Rd
 57051c656a17c7be27f38816fddc0b4c *man/isValidGraph.Rd
@@ -100,7 +100,7 @@ cd8a6e38249822fc7c54c66b96a6a967 *man/mcor.Rd
 1c7ed5406ee7aed47987da8900ab144e *man/opt.target.Rd
 088eee045f4ee07c7fd39971203f4c18 *man/optAdjSet.Rd
 67426f4203b1334a56a8715423083745 *man/pag2mag.Rd
-dd33bd15970a3b76a6c47147cc991c2b *man/pc.Rd
+f67ca974ccf8e71a2e62cec316c4da7e *man/pc.Rd
 01d980deb40fe93c7e3c2f5039882f9d *man/pc.cons.intern.Rd
 342566226ce52d9795a928ef44a7ca3c *man/pcAlgo-class.Rd
 143110b2d6797b5ef727a5c05d2790f8 *man/pcAlgo.Rd
@@ -202,4 +202,4 @@ b9564ddd59a06782310989e8f33db55e *vignettes/jsslogo.jpg
 c9b7539de0d214a8ec11c907472128fc *vignettes/pcalgDoc-concordance.tex
 6581d4416f174d4d6f07a2a2d4b9d3cb *vignettes/pcalgDoc.Rnw
 86183e3233b77b06b41111dd207131ea *vignettes/vignette2018-concordance.tex
-65ba145dfc8ae12f50c95d77a8bf8600 *vignettes/vignette2018.Rnw
+e2e81e4c1014989c895e5764846e7a3e *vignettes/vignette2018.Rnw
diff --git a/NAMESPACE b/NAMESPACE
index 8ad3373..51fb60f 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -9,7 +9,10 @@ importFrom("stats", cor, cor.test, cov, cov2cor,
 
 importFrom("utils", combn, str)
 
-importFrom("graphics", par, plot.default, plot, title)
+importFrom("graphics", par, plot, title)
+## For R >= 4.0.0, plot is in 'base' (but in 'graphics', too)
+## if(getRversion() < "4.0.0")
+##     importFrom("graphics", plot)
 
 importMethodsFrom("methods", coerce, show)
 importFrom("methods",
diff --git a/R/lingamFuns.R b/R/lingamFuns.R
index d646c35..3a92058 100644
--- a/R/lingamFuns.R
+++ b/R/lingamFuns.R
@@ -11,7 +11,7 @@
 ## details.
 ##
 ## You should have received a copy of the GNU General Public License along with
-## this program; if not, see <http://www.gnu.org/licenses/>.
+## this program; if not, see <https://www.gnu.org/licenses/>.
 ##################################################
 ## exported
 ##################################################
diff --git a/R/pcalg.R b/R/pcalg.R
index a5f8342..ac92df1 100644
--- a/R/pcalg.R
+++ b/R/pcalg.R
@@ -5023,7 +5023,7 @@ udag2pag <- function(pag, sepset, rules = rep(TRUE,10), unfVect = NULL, verbose
                     counterX1 <- 0
                     while (counterX1 < length(indX) && pag[c, a] == 1) {
                       counterX1 <- counterX1 + 1
-                      first.pos <- indA[counterX1]
+                      first.pos <- indX[counterX1]
                       indX2 <- setdiff(indX, first.pos)
                       counterX2 <- 0
                       while (counterX2 < length(indX2) && pag[c, a] == 1) {
diff --git a/build/vignette.rds b/build/vignette.rds
index c237519..8603219 100644
Binary files a/build/vignette.rds and b/build/vignette.rds differ
diff --git a/inst/CITATION b/inst/CITATION
index d979156..f307868 100644
--- a/inst/CITATION
+++ b/inst/CITATION
@@ -12,13 +12,13 @@ citEntry(entry = "Article",
   volume       = "47",
   number       = "11",
   pages        = "1--26",
-  url          = "http://www.jstatsoft.org/v47/i11/",
+  url          = "https://www.jstatsoft.org/article/view/v047i11",
 
   textVersion  =
   paste("Markus Kalisch, Martin Maechler, Diego Colombo, Marloes H. Maathuis, Peter Buehlmann (2012).",
         "Causal Inference Using Graphical Models with the R Package pcalg.",
         "Journal of Statistical Software, 47(11), 1-26.",
-        "URL http://www.jstatsoft.org/v47/i11/.")
+        "URL https://www.jstatsoft.org/article/view/v047i11.")
 )
 
 citEntry(entry = "Article",
diff --git a/inst/doc/pcalgDoc.R b/inst/doc/pcalgDoc.R
index f28d07f..a4b59f0 100644
--- a/inst/doc/pcalgDoc.R
+++ b/inst/doc/pcalgDoc.R
@@ -1,5 +1,4 @@
 ### R code from vignette source 'pcalgDoc.Rnw'
-### Encoding: UTF-8
 
 ###################################################
 ### code chunk number 1: preliminaries
diff --git a/inst/doc/pcalgDoc.pdf b/inst/doc/pcalgDoc.pdf
index de05787..72409c5 100644
Binary files a/inst/doc/pcalgDoc.pdf and b/inst/doc/pcalgDoc.pdf differ
diff --git a/inst/doc/vignette2018.R b/inst/doc/vignette2018.R
index fa634bc..1e1ea72 100644
--- a/inst/doc/vignette2018.R
+++ b/inst/doc/vignette2018.R
@@ -1,5 +1,4 @@
 ### R code from vignette source 'vignette2018.Rnw'
-### Encoding: UTF-8
 
 ###################################################
 ### code chunk number 1: preliminaries
@@ -42,8 +41,7 @@ showF <- function(f, width = 50) {
 ###################################################
 ### code chunk number 4: exIntro1
 ###################################################
-library("pcalg")
-data("gmG") ## loads data set gmG8
+data("gmG", package = "pcalg") ## loads data sets gmG and gmG8
 
 
 ###################################################
@@ -58,8 +56,6 @@ skel.gmG8 <- skeleton(suffStat, indepTest = gaussCItest,
 ###################################################
 ### code chunk number 6: exIntroPC
 ###################################################
-suffStat <- list(C = cor(gmG8$x), n = nrow(gmG8$x))
-varNames <- gmG8$g@nodes
 pc.gmG8 <- pc(suffStat, indepTest = gaussCItest,
               labels = varNames, alpha = 0.01)
 
@@ -88,7 +84,7 @@ fci.gmL <- fci(suffStat, indepTest=gaussCItest,
 ###################################################
 stopifnot(require(Rgraphviz))# needed for all our graph plots
 par(mfrow = c(1,2))
-plot(gmL$g) ; box(col="gray")
+plot(gmL$g)  ; box(col="gray")
 plot(fci.gmL); box(col="gray")
 
 
@@ -410,7 +406,7 @@ gac(mFig1, x,y, z=c(2,4),    type)
 
 
 ###################################################
-### code chunk number 40: vignette2018.Rnw:1088-1093
+### code chunk number 40: vignette2018.Rnw:1087-1092
 ###################################################
 mFig3a <- matrix(c(0,1,0,0, 0,0,1,1, 0,0,0,1, 0,0,1,0), 4,4)
 type <- "pdag"
diff --git a/inst/doc/vignette2018.Rnw b/inst/doc/vignette2018.Rnw
index ff34e9e..4e68969 100644
--- a/inst/doc/vignette2018.Rnw
+++ b/inst/doc/vignette2018.Rnw
@@ -146,8 +146,7 @@ The dataset $gmG8$ contains $n=5000$ observations of $p = 8$
 continuous variables with a multivariate Gaussian distribution.
 
 <<exIntro1>>=
-library("pcalg")
-data("gmG") ## loads data set gmG8
+data("gmG", package = "pcalg") ## loads data sets gmG and gmG8
 @
 
 In this example, the predefined function
@@ -168,12 +167,12 @@ Finding the skeleton is also the first step in the algorithms FCI, RFCI and FCI+
 
 The PC-algorithm is implemented in function \texttt{pc()}. The arguments follow closely the arguments of \texttt{skeleton()}, i.e., the most important arguments consist of an conditional independence test and a suitable sufficient statistic.
 
-We continue the previous example and illustrate function \texttt{pc()} using the built-in dataset $gmG8$. The result is shown in
+%  vvvvvvvvvvvvvvvvvvvvvvvvvvvvv
+We continue the previous example and illustrate function \texttt{pc()} using the built-in dataset $gmG8$.
+The result is shown in
 Fig.~\ref{fig:intro1} (bidirected edges need to be interpreted as undirected edges in the resulting CPDAG).
 
 <<exIntroPC>>=
-suffStat <- list(C = cor(gmG8$x), n = nrow(gmG8$x))
-varNames <- gmG8$g@nodes
 pc.gmG8 <- pc(suffStat, indepTest = gaussCItest,
               labels = varNames, alpha = 0.01)
 @
@@ -264,7 +263,7 @@ fci.gmL <- fci(suffStat, indepTest=gaussCItest,
 <<exIntroPlotFCI, echo=FALSE, fig=TRUE, fig.width=5, fig.height=2.5>>=
 stopifnot(require(Rgraphviz))# needed for all our graph plots
 par(mfrow = c(1,2))
-plot(gmL$g) ; box(col="gray")
+plot(gmL$g)  ; box(col="gray")
 plot(fci.gmL); box(col="gray")
 @
 \caption{True causal DAG (left) and estimated PAG on the observed nodes with the labels $2$, $3$, $4$ and $5$ (right).}
diff --git a/inst/doc/vignette2018.pdf b/inst/doc/vignette2018.pdf
index e8a8041..4141e81 100644
Binary files a/inst/doc/vignette2018.pdf and b/inst/doc/vignette2018.pdf differ
diff --git a/man/fci.Rd b/man/fci.Rd
index 8dfa917..e131c32 100644
--- a/man/fci.Rd
+++ b/man/fci.Rd
@@ -221,7 +221,7 @@ fci(suffStat, indepTest, alpha, labels, p,
   M.Kalisch, M. Maechler, D. Colombo, M. H. Maathuis,
   P. Buehlmann (2012). Causal Inference Using Graphical Models with
   the R Package pcalg. \emph{Journal of Statistical Software}
-  \bold{47(11)} 1--26, \url{http://www.jstatsoft.org/v47/i11/}.
+  \bold{47(11)} 1--26, \url{https://www.jstatsoft.org/v47/i11/}.
 
   P. Spirtes (2001). An anytime algorithm for causal inference. \emph{In
   Proc. of the Eighth International Workshop on Artifiial Intelligence
diff --git a/man/ida.Rd b/man/ida.Rd
index 7708d45..cd9c650 100644
--- a/man/ida.Rd
+++ b/man/ida.Rd
@@ -176,7 +176,7 @@ ida(x.pos, y.pos, mcov, graphEst, method = c("local","optimal","global"),
   Peter Buehlmann (2012).
   Causal inference using graphical models with the R-package pcalg.
   \emph{Journal of Statistical Software} \bold{47}(11) 1--26,
-  \url{http://www.jstatsoft.org/v47/i11/}.
+  \url{https://www.jstatsoft.org/v47/i11/}.
 
   Pearl (2005). \emph{Causality. Models, reasoning and inference}.
   Cambridge University Press, New York.
diff --git a/man/pc.Rd b/man/pc.Rd
index 2f89bd0..deb8e1b 100644
--- a/man/pc.Rd
+++ b/man/pc.Rd
@@ -237,7 +237,7 @@ pc(suffStat, indepTest, alpha, labels, p,
   M. Kalisch, M. Maechler, D. Colombo, M.H. Maathuis and P. Buehlmann
   (2012). Causal Inference Using Graphical Models with the R Package
   pcalg. \emph{Journal of Statistical Software} \bold{47(11)} 1--26,
-  \url{http://www.jstatsoft.org/v47/i11/}.
+  \url{https://www.jstatsoft.org/v47/i11/}.
 
   M. Kalisch and P. Buehlmann (2007).
   Estimating high-dimensional directed acyclic graphs with the PC-algorithm.
diff --git a/vignettes/vignette2018.Rnw b/vignettes/vignette2018.Rnw
index ff34e9e..4e68969 100644
--- a/vignettes/vignette2018.Rnw
+++ b/vignettes/vignette2018.Rnw
@@ -146,8 +146,7 @@ The dataset $gmG8$ contains $n=5000$ observations of $p = 8$
 continuous variables with a multivariate Gaussian distribution.
 
 <<exIntro1>>=
-library("pcalg")
-data("gmG") ## loads data set gmG8
+data("gmG", package = "pcalg") ## loads data sets gmG and gmG8
 @
 
 In this example, the predefined function
@@ -168,12 +167,12 @@ Finding the skeleton is also the first step in the algorithms FCI, RFCI and FCI+
 
 The PC-algorithm is implemented in function \texttt{pc()}. The arguments follow closely the arguments of \texttt{skeleton()}, i.e., the most important arguments consist of an conditional independence test and a suitable sufficient statistic.
 
-We continue the previous example and illustrate function \texttt{pc()} using the built-in dataset $gmG8$. The result is shown in
+%  vvvvvvvvvvvvvvvvvvvvvvvvvvvvv
+We continue the previous example and illustrate function \texttt{pc()} using the built-in dataset $gmG8$.
+The result is shown in
 Fig.~\ref{fig:intro1} (bidirected edges need to be interpreted as undirected edges in the resulting CPDAG).
 
 <<exIntroPC>>=
-suffStat <- list(C = cor(gmG8$x), n = nrow(gmG8$x))
-varNames <- gmG8$g@nodes
 pc.gmG8 <- pc(suffStat, indepTest = gaussCItest,
               labels = varNames, alpha = 0.01)
 @
@@ -264,7 +263,7 @@ fci.gmL <- fci(suffStat, indepTest=gaussCItest,
 <<exIntroPlotFCI, echo=FALSE, fig=TRUE, fig.width=5, fig.height=2.5>>=
 stopifnot(require(Rgraphviz))# needed for all our graph plots
 par(mfrow = c(1,2))
-plot(gmL$g) ; box(col="gray")
+plot(gmL$g)  ; box(col="gray")
 plot(fci.gmL); box(col="gray")
 @
 \caption{True causal DAG (left) and estimated PAG on the observed nodes with the labels $2$, $3$, $4$ and $5$ (right).}