diff --git a/DESCRIPTION b/DESCRIPTION
index fbdecc7d31d17abdbb0526ac101d8c922e7d714a..30eec4b8816e4ca3a95d06be5994f79f2b7b2acd 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -15,15 +15,14 @@ Imports:
     R6 (>= 2.2.2),
     splines (>= 3.1.1),
     pbs,
-    digest,
+    digest
 LinkingTo: Rcpp, RcppArmadillo
 Suggests:
     knitr,
     rmarkdown,
     R.rsp,
     testthat (>= 2.1.0),
-    data.table,
-    plotly
+    data.table
 VignetteBuilder: knitr
 RoxygenNote: 7.1.1
 URL: http://onlineforecasting.org
diff --git a/R/plot_ts.R b/R/plot_ts.R
index 00103dacef60acaaae782b13696c63add7c70fdd..a05b6af63b8ec6934265a6b4f59e4770658b5d31 100644
--- a/R/plot_ts.R
+++ b/R/plot_ts.R
@@ -60,17 +60,6 @@
 #' names(L[[2]])
 #'
 #'
-#' # Use plotly
-#' \donttest{library(plotly)
-#' L <- plot_ts(D, c("heatload","Ta"), kseq=c(1,24), usely=TRUE, xlab="Time",
-#'              ylabs=c("Heat (kW)","Temperature (C)"))
-#'
-#' # From plotly the figures are returned and can be further manipulated
-#' # e.g. put the legend in the top by
-#' L[[length(L)]] <- L[[length(L)]] %>% layout(legend = list(x = 100, y = 0.98))
-#' print(subplot(L, shareX=TRUE, nrows=length(L), titleY = TRUE))
-#' }
-#'
 #' @rdname plot_ts
 #' @export
 plot_ts <- function(object, patterns=".*", xlim = NA, ylims = NA, xlab = "", ylabs = NA,
@@ -474,11 +463,6 @@ plot_ts_series <- function(data, pattern, iplot = 1,
 #' # Plot it
 #' plot_ts(fit1)
 #'
-#' # Plot it with plotly
-#' \donttest{
-#' plot_ts(fit1, usely=TRUE)
-#' }
-#'
 #' # Return the data
 #' Dplot <- plot_ts(fit1)
 #'
diff --git a/R/plotly_ts.R b/R/plotly_ts.R
index 5f34a218b61e7b25be3eca6bf82744cb22777f9f..209ba281a2985378b480fd00a7845f490084a4cb 100644
--- a/R/plotly_ts.R
+++ b/R/plotly_ts.R
@@ -9,20 +9,17 @@
 #'
 #' Simply the same as \code{\link{plot_ts}()} with \code{usely=TRUE}, such that plotly is used.
 #'
-#' The \code{plotly} package must be loaded.
+#' The \code{plotly} package must be installed and loaded.
 #'
 #' Note that the plot parameters set with \code{\link{par_ts}()} have no effect on the \code{plotly} plots.
 #'
+#' See \url{http://https://onlineforecasting.org/vignettes/nice-tricks.html}.
+#' 
 #' @rdname plot_ts
 #' @examples
 #'
-#' \donttest{
-#' D <- Dbuilding
-#' plotly_ts(D, c("heatload","Ta"), kseq=c(1,24))
-#' plotly_ts(D, c("heatload","Ta"), kseq=c(1,24))
-#' plotly_ts(D, c("heatload","Ta$|Taobs$"), kseq=c(1,24))
-#' }
-#'
+#' # See the website link above
+#' 
 #' @export
 
 plotly_ts <- function(object, patterns=".*", xlim = NA, ylims = NA, xlab = "", ylabs = NA,
diff --git a/R/score_for_k.R b/R/score.R
similarity index 92%
rename from R/score_for_k.R
rename to R/score.R
index 6b9cea980ce759697539a464534f982781699727..e394c3d99056cdd1cc84814311181a57a21bd796 100644
--- a/R/score_for_k.R
+++ b/R/score.R
@@ -24,7 +24,7 @@
 #' Resid <- residuals(Yhat, y)
 #'
 #' # Calculate the score for the k1 horizon
-#' score(Resid)$val
+#' score(Resid)$scoreval
 #'
 #' # The first values were excluded, since there are NAs
 #' head(Resid)
@@ -50,10 +50,10 @@ score <- function(Residuals, scoreperiod = NA, usecomplete = TRUE, scorefun = rm
         scoreperiod <- scoreperiod & complete.cases(Residuals)
     }
     # Calculate the objective function for each horizon
-    val <- sapply(1:ncol(Residuals), function(i){
+    scoreval <- sapply(1:ncol(Residuals), function(i){
         scorefun(Residuals[scoreperiod,i])
     })
-    nams(val) <- gsub("h","k",nams(Residuals))
+    nams(scoreval) <- gsub("h","k",nams(Residuals))
     # 
-    return(list(val=val,scoreperiod=scoreperiod))
+    return(list(scoreval=scoreval,scoreperiod=scoreperiod))
 }
diff --git a/vignettes/forecast-evaluation.Rmd b/vignettes/forecast-evaluation.Rmd
index 8da010ea71d6d8345fed6a5012e57d46b90271be..f40ad815dc04a0efea2c4192d147c8fde69717fa 100644
--- a/vignettes/forecast-evaluation.Rmd
+++ b/vignettes/forecast-evaluation.Rmd
@@ -326,14 +326,14 @@ Now the residuals can be calculated and the score:
 # Use the residuals function
 R <- residuals(D$Yhat1, D$y)
 # And the score as a function of the horizon
-score(R, scoreperiod=ok)$val
+score(R, scoreperiod=ok)$scoreval
 ```
 
 
 Calculated the score (default is RMSE) for all models:
 ```{r}
 RMSE <- sapply(nms, function(nm){
-    score(residuals(D[[nm]],D$y), ok)$val
+    score(residuals(D[[nm]],D$y), ok)$scoreval
 })
 ```
     
@@ -386,7 +386,7 @@ fittmp <- rls_fit(model$prm, model, D)
 
 Finally, the score can be calculated on the period following the train period by:
 ```{r scorefit}
-score_fit(fittmp, !D$trainperiod)$val
+score_fit(fittmp, !D$trainperiod)$scoreval
 ```
 
 In this way it's rather easy to set up different schemes, like optimizing the