diff --git a/.Rbuildignore b/.Rbuildignore
old mode 100644
new mode 100755
index 7ef3a46..9fd098b
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -10,4 +10,5 @@ tar.gz$
 ^appveyor\.yml$
 ^\.github$
 .DS_Store
-^revdep$
\ No newline at end of file
+^revdep$
+^CRAN-SUBMISSION$
diff --git a/.github/.gitignore b/.github/.gitignore
old mode 100644
new mode 100755
diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
old mode 100644
new mode 100755
diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
new file mode 100644
index 0000000..fbdcfac
--- /dev/null
+++ b/CRAN-SUBMISSION
@@ -0,0 +1,3 @@
+Version: 2.5.5
+Date: 2025-06-08 18:57:19 UTC
+SHA: 5f47d4cf5aa147c9f310e2917e566d2480ddd527
diff --git a/DESCRIPTION b/DESCRIPTION
old mode 100644
new mode 100755
index dba61d1..4a23df2
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -7,11 +7,11 @@ Description: Estimate significance of importance metrics
     metrics for each predictor variable and p-value of
     observed. Provides summary and visualization functions for 'randomForest' 
     results.
-Version: 2.5.4
+Version: 2.5.6
 Authors@R: c(
-  EA = person("Eric", "Archer", email = "eric.archer@noaa.gov", role = c("aut", "cre")))
-URL: https://github.com/EricArcher/rfPermute
-BugReports: https://github.com/EricArcher/rfPermute/issues
+  EA = person("Eric", "Archer", email = "eric.ivan.archer@gmail.com", role = c("aut", "cre")))
+URL: https://github.com/SWFSC/rfPermute
+BugReports: https://github.com/SWFSC/rfPermute/issues
 Depends:
     R (>= 4.1.0)
 Imports:
@@ -20,9 +20,9 @@ Imports:
     ggplot2 (>= 3.3),
     grDevices,
     gridExtra,
-    magrittr (>= 2.0),
     methods,
     parallel,
+    pROC,
     randomForest (>= 4.6),
     rlang,
     scales,
@@ -52,7 +52,8 @@ Collate:
     plotVotes.R
     balancedSampsize.R
     cleanRFdata.R
+    rfROC.R
 LazyData: TRUE
 License: GPL (>= 2)
 Encoding: UTF-8
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3
diff --git a/NAMESPACE b/NAMESPACE
index d06f4cb..84aaec5 100755
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -27,7 +27,7 @@ export(plotTrace)
 export(plotVotes)
 export(rfPermute)
 export(rfPermuteTutorial)
-importFrom(magrittr,"%>%")
+export(rfROC)
 importFrom(methods,new)
 importFrom(randomForest,importance)
 importFrom(randomForest,randomForest)
diff --git a/R/balancedSampsize.R b/R/balancedSampsize.R
old mode 100644
new mode 100755
diff --git a/R/casePredictions.R b/R/casePredictions.R
old mode 100644
new mode 100755
diff --git a/R/classPriors.R b/R/classPriors.R
old mode 100644
new mode 100755
diff --git a/R/cleanRFdata.R b/R/cleanRFdata.R
old mode 100644
new mode 100755
diff --git a/R/confusionMatrix.R b/R/confusionMatrix.R
old mode 100644
new mode 100755
index 3f7f731..c6ba31b
--- a/R/confusionMatrix.R
+++ b/R/confusionMatrix.R
@@ -72,6 +72,7 @@ confusionMatrix <- function(x, conf.level = 0.95, threshold = NULL) {
   cm <- rbind(cm, Overall = rep(NA, ncol(cm)))
   pct.correct <- (correct.n / all.n) * 100
   cbind(
+    n = all.n,
     cm, 
     pct.correct = pct.correct[rownames(cm)], 
     ci[rownames(cm), , drop = FALSE],
@@ -88,8 +89,8 @@ plotConfMat <- function(x, title = NULL, plot = TRUE) {
   if(rf$type == "regression") stop("'rf' must be of a classification model")
   
   conf <- .confMat(rf)
-  pct.correct <- (100 * sum(diag(conf)) / sum(conf)) %>% 
-    round(0) %>% 
+  pct.correct <- (100 * sum(diag(conf)) / sum(conf)) |> 
+    round(0) |> 
     paste0("% correct")
   title <- if(is.null(title)) {
     pct.correct 
@@ -99,18 +100,18 @@ plotConfMat <- function(x, title = NULL, plot = TRUE) {
   freq <- rowSums(conf)
   rownames(conf) <- paste0(names(freq), " (", freq, ")")
   
-  p <- conf %>% 
-    prop.table(1) %>% 
-    as.data.frame %>% 
-    tibble::rownames_to_column("observed") %>% 
-    tidyr::gather("predicted", "prop", -.data$observed) %>% 
+  p <- conf |> 
+    prop.table(1) |> 
+    as.data.frame() |> 
+    tibble::rownames_to_column("observed") |> 
+    tidyr::gather("predicted", "prop", -.data$observed) |> 
     dplyr::mutate(
       observed = factor(.data$observed),
       observed = stats::reorder(.data$observed, dplyr::desc(.data$observed)),
       predicted = factor(.data$predicted)
-    ) %>% 
-    ggplot2::ggplot(ggplot2::aes_string("predicted", "observed")) +
-    ggplot2::geom_raster(ggplot2::aes_string(fill = "prop")) +
+    ) |> 
+    ggplot2::ggplot(ggplot2::aes(x = .data$predicted, y = .data$observed)) +
+    ggplot2::geom_raster(ggplot2::aes(fill = .data$prop)) +
     ggplot2::scale_fill_viridis_c(
       option = "magma", 
       direction = -1, 
@@ -121,7 +122,8 @@ plotConfMat <- function(x, title = NULL, plot = TRUE) {
     ggplot2::guides(fill = ggplot2::guide_colorbar(title = "Proportion")) +
     ggplot2::theme(
       axis.text.x.top = ggplot2::element_text(angle = 45, hjust = 0),
-      panel.background = ggplot2::element_blank()
+      panel.background = ggplot2::element_blank(),
+      legend.key.height = ggplot2::unit(1, 'null')
     )
   
   if(plot) print(p)
diff --git a/R/dataSets.R b/R/dataSets.R
old mode 100644
new mode 100755
diff --git a/R/importance.R b/R/importance.R
old mode 100644
new mode 100755
index 25128a9..31c8f76
--- a/R/importance.R
+++ b/R/importance.R
@@ -137,12 +137,12 @@ plotImportance <- function(x, plot.type = c("bar", "heatmap"), imp.type = NULL,
         pval = if(inherits(x, "rfPermute")) {
           x$pval[rownames(imp.mat), i, sc]
         } else Inf
-      ) %>% 
-        tibble::rownames_to_column("pred") %>% 
+      ) |> 
+        tibble::rownames_to_column("pred") |> 
         dplyr::mutate(
           is.sig = .data$pval <= alpha,
           pred = stats::reorder(.data$pred, .data$imp)
-        ) %>% 
+        ) |> 
         dplyr::arrange(-.data$imp)
       if(sig.only) imp.df <- imp.df[imp.df$is.sig, ]
       n <- min(n, nrow(imp.df))
@@ -151,7 +151,7 @@ plotImportance <- function(x, plot.type = c("bar", "heatmap"), imp.type = NULL,
       
       ggplot2::ggplot(
         imp.df, 
-        ggplot2::aes_string(x = "pred", y = "imp", fill = "is.sig") 
+        ggplot2::aes(x = .data$pred, y = .data$imp, fill = .data$is.sig) 
       ) + 
         ggplot2::geom_bar(stat = "identity") +
         ggplot2::coord_flip() + 
@@ -174,17 +174,17 @@ plotImportance <- function(x, plot.type = c("bar", "heatmap"), imp.type = NULL,
     preds <- rev(rev(preds)[1:n])
     imp.df <- data.frame(imp.mat[preds, imp.type, drop = FALSE], check.names = FALSE)
     if(ranks) for(i in imp.type) imp.df[[i]] <- rank(-imp.df[[i]])
-    imp.df <- imp.df %>% 
-      tibble::rownames_to_column("pred") %>% 
-      tidyr::pivot_longer(-.data$pred, names_to = "type") %>% 
+    imp.df <- imp.df |> 
+      tibble::rownames_to_column("pred") |> 
+      tidyr::pivot_longer(-.data$pred, names_to = "type") |> 
       dplyr::mutate(
         type = factor(.data$type, levels = imp.type),
         pred = factor(.data$pred, levels = preds)
       )
     
     # create plot
-    g <- ggplot2::ggplot(imp.df, ggplot2::aes_string("type", "pred")) +
-      ggplot2::geom_raster(ggplot2::aes_string(fill = "value")) + 
+    g <- ggplot2::ggplot(imp.df, ggplot2::aes(x = .data$type, y = .data$pred)) +
+      ggplot2::geom_raster(ggplot2::aes(fill = .data$value)) + 
       ggplot2::theme(panel.background = ggplot2::element_blank())
     g <- g + if(ranks) {
       ggplot2::scale_fill_gradient2(
@@ -236,4 +236,4 @@ plotImportance <- function(x, plot.type = c("bar", "heatmap"), imp.type = NULL,
     }
   }
   invisible(imp.plot)
-}
\ No newline at end of file
+}
diff --git a/R/internals.R b/R/internals.R
old mode 100644
new mode 100755
diff --git a/R/pctCorrect.R b/R/pctCorrect.R
old mode 100644
new mode 100755
diff --git a/R/plotImpPreds.R b/R/plotImpPreds.R
old mode 100644
new mode 100755
index 31a34a5..603b922
--- a/R/plotImpPreds.R
+++ b/R/plotImpPreds.R
@@ -68,10 +68,10 @@ plotImpPreds <- function(x, df, class.col, imp.type = NULL, max.vars = 16,
   df$.class. <- factor(df[[class.col]])
   df <- df[, c(".class.", imp.vars)]
   
-  p <- df %>%
-    tidyr::pivot_longer(-.data$.class., names_to = "var") %>% 
-    dplyr::mutate(var = factor(.data$var, levels = imp.vars)) %>% 
-    ggplot2::ggplot(ggplot2::aes_string(".class.", "value")) +
+  p <- df |>
+    tidyr::pivot_longer(-.data$.class., names_to = "var") |> 
+    dplyr::mutate(var = factor(.data$var, levels = imp.vars)) |> 
+    ggplot2::ggplot(ggplot2::aes(x = .data$.class., y = .data$value)) +
     ggplot2::geom_violin(alpha = violin.alpha) +
     ggplot2::geom_jitter(size = size, alpha = point.alpha, width = 0.25, height = 0) +
     ggplot2::facet_wrap(~ var, scales = "free_y") +
@@ -80,4 +80,4 @@ plotImpPreds <- function(x, df, class.col, imp.type = NULL, max.vars = 16,
   
   if(plot) print(p)
   invisible(p)
-}
\ No newline at end of file
+}
diff --git a/R/plotInbag.R b/R/plotInbag.R
old mode 100644
new mode 100755
index 8dc829f..3f39008
--- a/R/plotInbag.R
+++ b/R/plotInbag.R
@@ -72,7 +72,7 @@ plotInbag <- function(x, bins = 10, replace = TRUE, sampsize = NULL,
   pct <- ((rf$ntree - rf$oob.times) / rf$ntree) * 100
   bins <- max(bins, floor(length(pct) / 5))
   p <- data.frame(pct = pct) |> 
-    ggplot2::ggplot(ggplot2::aes_string("pct")) +
+    ggplot2::ggplot(ggplot2::aes(x = .data$pct)) +
     ggplot2::geom_histogram(bins = bins) +
     ggplot2::labs(
       x = "Percent of trees where sample was inbag",
diff --git a/R/plotNull.R b/R/plotNull.R
old mode 100644
new mode 100755
index a1ef574..4c3eff4
--- a/R/plotNull.R
+++ b/R/plotNull.R
@@ -65,17 +65,17 @@ plotNull <- function(x, preds = NULL, imp.type = NULL, scale = TRUE,
       imp.type
     )
     
-    df <- sapply(imp.type, function(i) x$null.dist[[sc]][pr, i, ]) %>% 
-      as.data.frame() %>% 
-      tidyr::gather("imp.type", "importance") %>% 
+    df <- sapply(imp.type, function(i) x$null.dist[[sc]][pr, i, ]) |> 
+      as.data.frame() |> 
+      tidyr::gather("imp.type", "importance") |> 
       dplyr::mutate(imp.type = factor(labels[imp.type], levels = labels))
-    obs <- imp[pr, imp.type, drop = FALSE] %>% 
-      as.data.frame() %>% 
-      tibble::rownames_to_column("predictor") %>% 
-      tidyr::gather("imp.type", "importance", -.data$predictor) %>% 
+    obs <- imp[pr, imp.type, drop = FALSE] |> 
+      as.data.frame() |> 
+      tibble::rownames_to_column("predictor") |> 
+      tidyr::gather("imp.type", "importance", -.data$predictor) |> 
       dplyr::mutate(imp.type = factor(labels[imp.type], levels = labels))
     
-    p <- ggplot2::ggplot(df, ggplot2::aes_string("importance")) 
+    p <- ggplot2::ggplot(df, ggplot2::aes(x = .data$importance)) 
     p <- if(plot.type == "hist") {
         p + ggplot2::geom_histogram() + ggplot2::ylab("Count")
       } else {
@@ -85,7 +85,7 @@ plotNull <- function(x, preds = NULL, imp.type = NULL, scale = TRUE,
       ggplot2::xlab("Importance") + 
       ggplot2::ggtitle(pr) +
       ggplot2::geom_vline(
-        ggplot2::aes_string(xintercept = "importance"), 
+        ggplot2::aes(xintercept = .data$importance), 
         color = "red", data = obs
       ) +
       ggplot2::facet_wrap(~ imp.type, scales = "free")
@@ -93,4 +93,4 @@ plotNull <- function(x, preds = NULL, imp.type = NULL, scale = TRUE,
   
   if(plot) for(p in g) print(p)
   invisible(g)
-}
\ No newline at end of file
+}
diff --git a/R/plotPredictedProbs.R b/R/plotPredictedProbs.R
old mode 100644
new mode 100755
index 39e6603..6a6c6af
--- a/R/plotPredictedProbs.R
+++ b/R/plotPredictedProbs.R
@@ -25,16 +25,16 @@ plotPredictedProbs <- function(x, bins = 30, plot = TRUE) {
   rf <- as.randomForest(x)
   if(rf$type == "regression") stop("'rf' must be of a classification model")
   
-  p <- rf$votes %>% 
-    as.data.frame %>% 
+  p <- rf$votes |> 
+    as.data.frame() |> 
     cbind(
       class = as.character(rf$y),
       predicted = as.character(rf$predicted)
-    ) %>% 
-    tidyr::gather("pred.class", "prob", -.data$class, -.data$predicted) %>% 
-    dplyr::filter(.data$predicted == .data$pred.class) %>% 
-    dplyr::mutate(correct = .data$class == .data$predicted) %>% 
-    ggplot2::ggplot(ggplot2::aes_string("prob", fill = "class")) +
+    ) |> 
+    tidyr::gather("pred.class", "prob", -.data$class, -.data$predicted) |> 
+    dplyr::filter(.data$predicted == .data$pred.class) |> 
+    dplyr::mutate(correct = .data$class == .data$predicted) |> 
+    ggplot2::ggplot(ggplot2::aes(.data$prob, fill = .data$class)) +
     ggplot2::geom_histogram(bins = bins) +
     ggplot2::facet_wrap(~ predicted) +
     ggplot2::labs(x = "Assignment probability", y = "Frequency") +
@@ -42,4 +42,4 @@ plotPredictedProbs <- function(x, bins = 30, plot = TRUE) {
   
   if(plot) print(p)
   invisible(p)
-}
\ No newline at end of file
+}
diff --git a/R/plotProximity.R b/R/plotProximity.R
old mode 100644
new mode 100755
index edf2da6..1cf5770
--- a/R/plotProximity.R
+++ b/R/plotProximity.R
@@ -77,7 +77,7 @@ plotProximity <- function(x, dim.x = 1, dim.y = 2, class.cols = NULL,
   mds.df <- data.frame(prox.mds, class = rf$y, predicted = rf$predicted)
   colnames(mds.df)[1:2] <- c("x", "y")
   
-  g <- ggplot2::ggplot(mds.df, ggplot2::aes_(~x, ~y, color = ~class)) 
+  g <- ggplot2::ggplot(mds.df, ggplot2::aes(.data$x, .data$y, color = .data$class)) 
   
   # Origin axes
   g <- g + 
@@ -93,7 +93,7 @@ plotProximity <- function(x, dim.x = 1, dim.y = 2, class.cols = NULL,
       group.type, 
       ellipse = {
         g <- g + ggplot2::stat_ellipse(
-          ggplot2::aes_(fill = ~class), 
+          ggplot2::aes(fill = .data$class), 
           geom = "polygon",
           alpha = group.alpha,
           level = ellipse.level,
@@ -105,7 +105,7 @@ plotProximity <- function(x, dim.x = 1, dim.y = 2, class.cols = NULL,
           cl.df <- mds.df[mds.df$class == cl, ]
           i <- grDevices::chull(cl.df$x, cl.df$y)
           g <- g + ggplot2::geom_polygon(
-            ggplot2::aes_(fill = ~class), 
+            ggplot2::aes(fill = .data$class), 
             data = cl.df[c(i, i[1]), ], 
             alpha = group.alpha,
             show.legend = legend.type == "legend"
@@ -133,7 +133,7 @@ plotProximity <- function(x, dim.x = 1, dim.y = 2, class.cols = NULL,
   # Predicted circles
   if(!is.null(circle.size)) {
     g <- g + ggplot2::geom_point(
-      ggplot2::aes_(color = ~predicted), 
+      ggplot2::aes(color = .data$predicted), 
       shape = 21, 
       size = circle.size,
       stroke = circle.border,
@@ -152,13 +152,13 @@ plotProximity <- function(x, dim.x = 1, dim.y = 2, class.cols = NULL,
   # Class labels
   if(legend.type == "label") {
     g <- g + ggplot2::geom_label(
-      ggplot2::aes_(label = ~class), 
-      data = mds.df %>% 
-        dplyr::group_by(.data$class) %>% 
+      ggplot2::aes(label = .data$class), 
+      data = mds.df |> 
+        dplyr::group_by(.data$class) |> 
         dplyr::summarize(
           x = mean(.data$x),
           y = mean(.data$y)
-        ) %>% 
+        ) |> 
         dplyr::ungroup(),
       fill = "white",
       alpha = label.alpha,
@@ -193,4 +193,4 @@ plotProximity <- function(x, dim.x = 1, dim.y = 2, class.cols = NULL,
   
   if(plot) print(g)
   invisible(list(prox.mds = prox.mds, g = g))
-}
\ No newline at end of file
+}
diff --git a/R/plotTrace.R b/R/plotTrace.R
old mode 100644
new mode 100755
index 7688a70..cf5f2c5
--- a/R/plotTrace.R
+++ b/R/plotTrace.R
@@ -28,15 +28,17 @@ plotTrace <- function(x, pct.correct = TRUE, plot = TRUE) {
     names(class.cols) <- names(class.lines) <- c("OOB", levels(rf$y))
     
     df <- as.data.frame(rf$err.rate)
-    df %>% 
-      dplyr::mutate(trees = 1:dplyr::n()) %>% 
-      tidyr::gather("class", "error", -.data$trees) %>% 
+    df |> 
+      dplyr::mutate(trees = 1:dplyr::n()) |> 
+      tidyr::gather("class", "error", -.data$trees) |> 
       dplyr::mutate(
         class = factor(.data$class, levels = colnames(df)),
         error = if(pct.correct) (1 - .data$error) * 100 else .data$error * 100
-      ) %>% 
-      ggplot2::ggplot(ggplot2::aes_string("trees", "error", color = "class")) +
-      ggplot2::geom_line(ggplot2::aes_string(linetype = "class")) +
+      ) |> 
+      ggplot2::ggplot(
+        ggplot2::aes(x = .data$trees, y = .data$error, color = .data$class)
+      ) +
+      ggplot2::geom_line(ggplot2::aes(linetype = .data$class)) +
       ggplot2::scale_color_manual(values = class.cols) +
       ggplot2::scale_linetype_manual(values = class.lines) +
       ggplot2::labs(
@@ -45,8 +47,8 @@ plotTrace <- function(x, pct.correct = TRUE, plot = TRUE) {
       ) +
       ggplot2::theme(legend.title = ggplot2::element_blank())
   } else if(utils::hasName(rf, "mse")) {
-    data.frame(trees = 1:length(rf$mse), error = rf$mse) %>% 
-      ggplot2::ggplot(ggplot2::aes_string("trees", "error")) +
+    data.frame(trees = 1:length(rf$mse), error = rf$mse) |> 
+      ggplot2::ggplot(ggplot2::aes(x = .data$trees, y = .data$error)) +
       ggplot2::geom_line() +
       ggplot2::labs(x = "Trees", y = "Mean Squared Error")
   } else NULL
@@ -59,4 +61,4 @@ plotTrace <- function(x, pct.correct = TRUE, plot = TRUE) {
   }
   if(plot) print(p)
   invisible(p)
-}
\ No newline at end of file
+}
diff --git a/R/plotVotes.R b/R/plotVotes.R
old mode 100644
new mode 100755
diff --git a/R/rfPermute-package.R b/R/rfPermute-package.R
old mode 100644
new mode 100755
index ff7d5b6..4823b5a
--- a/R/rfPermute-package.R
+++ b/R/rfPermute-package.R
@@ -8,7 +8,6 @@
 #' 
 #' @importFrom randomForest randomForest
 #' @importFrom rlang .data
-#' @importFrom magrittr %>%
 #' @importFrom methods new
 #' 
 .onAttach <- function(libname, pkgname) {
diff --git a/R/rfPermute.R b/R/rfPermute.R
index f0110eb..af4a3ef 100755
--- a/R/rfPermute.R
+++ b/R/rfPermute.R
@@ -270,7 +270,6 @@ predict.rfPermute <- function(object, ...) predict(as.randomForest(object), ...)
 
 
 #' @rdname rfPermute
-#' @importFrom stats predict
 #' @export
 #' 
 rfPermuteTutorial <- function() {
diff --git a/R/rfROC.R b/R/rfROC.R
new file mode 100644
index 0000000..ee47a42
--- /dev/null
+++ b/R/rfROC.R
@@ -0,0 +1,110 @@
+#' @title Receiver Operator Curve
+#' @description Create a Receiver Operator Curve (ROC) for each class in a 
+#'   Random Forest model and (optionally) plot them.
+#' 
+#' @param x a \code{rfPermute} or \code{randomForest} model object.
+#' @param plot logical? Plot the curve?
+#' 
+#' @return \describe{ a list returned invisbly containing:
+#'  \item{\code{roc}}{a list containing ROC results from \code{\link[pROC]{roc}} for each class}
+#'  \item{\code{smry}}{a data fame summarizing the threshold and AUC for each class}
+#'  \item{\code{plot}}{the \code{ggplot} plot of the ROC curves}
+#'}
+#' 
+#' @author Eric Archer \email{eric.archer@@noaa.gov} 
+#' 
+#' @examples
+#' library(randomForest)
+#' data(mtcars)
+#'
+#' # random sampling with replacement
+#' rf <- randomForest(factor(am) ~ ., mtcars)
+#' 
+#' roc <- rfROC(rf)
+#' 
+#' roc$smry
+#' 
+#' @export
+#'
+rfROC <- function(x, plot = TRUE) {
+  rf <- as.randomForest(x)
+  if(rf$type != "classification") stop("'x' must be of a classification model")
+  
+  roc.list <- sapply(levels(rf$y), function(cls) {
+    class.roc <- pROC::roc(
+      as.numeric(rf$y == cls),
+      rf$votes[, cls], 
+      direction = "<",
+      quiet = TRUE
+    )
+    
+    df <- data.frame(
+      sensitivity = class.roc$sensitivities,
+      specificity = class.roc$specificities,
+      threshold = class.roc$thresholds
+    ) |> 
+      dplyr::mutate(
+        class = cls,
+        threshold = ifelse(.data$threshold == -Inf, 0, .data$threshold),
+        threshold = ifelse(.data$threshold == Inf, 1, .data$threshold)
+      ) |> 
+      dplyr::arrange(dplyr::desc(.data$specificity), .data$sensitivity, .data$threshold)
+    
+    list(roc = class.roc, df = df)
+  }, simplify = FALSE)
+  
+  smry <- do.call(
+    rbind,
+    lapply(roc.list, function(r) {
+      pROC::coords(
+        roc = r$roc, 
+        x = "best", 
+        ret = c("threshold", "sensitivity", "specificity")
+      ) |> 
+        dplyr::mutate(
+          auc = as.vector(pROC::auc(r$roc)),
+          class = unique(r$df$class)
+        ) |> 
+        dplyr::select(.data$class, dplyr::everything())
+    })
+  ) |> 
+    tibble::remove_rownames()
+  
+  p <- do.call(
+    rbind,
+    lapply(roc.list, function(x) x$df)
+  ) |> 
+    ggplot2::ggplot(ggplot2::aes(.data$specificity, .data$sensitivity)) +
+    ggplot2::annotate(
+      'segment', 
+      x = 1, 
+      y = 0, 
+      xend = 0, 
+      yend = 1, 
+      linetype = 'dashed', 
+      color = 'gray'
+    ) +
+    ggplot2::geom_line(ggplot2::aes(color = .data$threshold), linewidth = 2) +
+    ggplot2::geom_text(data = smry, label = 'T') +
+    ggplot2::labs(x = 'Specificity', y = 'Sensitivity') +
+    ggplot2::scale_color_distiller(
+      name = 'Threshold',
+      palette = 'Spectral',
+      limits = c(0, 1),
+      rescaler  = ~scales::rescale_mid(., mid = 0.5),
+      direction = -1
+    ) + 
+    ggplot2::scale_x_reverse() +
+    ggplot2::facet_wrap('class') +
+    ggplot2::theme_minimal() +
+    ggplot2::theme(legend.key.height = ggplot2::unit(1, "null")) 
+  if(plot) plot(p)
+  
+  print(smry)
+  
+  invisible(list(
+    roc = sapply(roc.list, function(x) x$roc, simplify = FALSE),
+    smry = smry,
+    p = p
+  ))
+}
\ No newline at end of file
diff --git a/R/summary.R b/R/summary.R
old mode 100644
new mode 100755
diff --git a/README.md b/README.md
index 8aa827c..4f4be7c 100755
--- a/README.md
+++ b/README.md
@@ -51,6 +51,7 @@ devtools::install_github('EricArcher/rfPermute')
 * `plotProximity` Plot Random Forest Proximity Scores   
 * `plotTrace` Trace of cumulative error rates in forest  
 * `plotVotes` Vote Distribution    
+* `rfROC` Receiver Operator Curve
  
 ### Miscellaneous functions
 * `combineRP` Combine rfPermute models  
@@ -59,7 +60,16 @@ devtools::install_github('EricArcher/rfPermute')
 
 ## Changelog
 
-### version 2.5.4 (on CRAN)
+### version 2.5.6 (devel)
+
+* switched aes_string() and aes_() for aes() in ggplot plots
+* added rfROC()
+
+### version 2.5.5 (on CRAN)
+
+* move of package to SWFSC/rfPermute as main GitHub repository
+
+### version 2.5.4
 
 * fixed print.rfPermute output for regression models.  
 
diff --git a/appveyor.yml b/appveyor.yml
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diff --git a/data/symb.metab.rda b/data/symb.metab.rda
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diff --git a/inst/rfPermute_Tutorial.html b/inst/rfPermute_Tutorial.html
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diff --git a/man/balancedSampsize.Rd b/man/balancedSampsize.Rd
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diff --git a/man/casePredictions.Rd b/man/casePredictions.Rd
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diff --git a/man/classPriors.Rd b/man/classPriors.Rd
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diff --git a/man/cleanRFdata.Rd b/man/cleanRFdata.Rd
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diff --git a/man/combineRP.Rd b/man/combineRP.Rd
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diff --git a/man/confusionMatrix.Rd b/man/confusionMatrix.Rd
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diff --git a/man/importance.Rd b/man/importance.Rd
old mode 100644
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diff --git a/man/pctCorrect.Rd b/man/pctCorrect.Rd
old mode 100644
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diff --git a/man/plotImpPreds.Rd b/man/plotImpPreds.Rd
old mode 100644
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diff --git a/man/plotInbag.Rd b/man/plotInbag.Rd
old mode 100644
new mode 100755
diff --git a/man/plotNull.Rd b/man/plotNull.Rd
old mode 100644
new mode 100755
diff --git a/man/plotPredictedProbs.Rd b/man/plotPredictedProbs.Rd
old mode 100644
new mode 100755
diff --git a/man/plotProximity.Rd b/man/plotProximity.Rd
old mode 100644
new mode 100755
diff --git a/man/plotTrace.Rd b/man/plotTrace.Rd
old mode 100644
new mode 100755
diff --git a/man/plotVotes.Rd b/man/plotVotes.Rd
old mode 100644
new mode 100755
diff --git a/man/rfPermute-package.Rd b/man/rfPermute-package.Rd
old mode 100644
new mode 100755
index 67f3e2e..8f34525
--- a/man/rfPermute-package.Rd
+++ b/man/rfPermute-package.Rd
@@ -10,13 +10,13 @@ Random Forest Predictor Importance Significance and Model Diagnostics.
 \seealso{
 Useful links:
 \itemize{
-  \item \url{https://github.com/EricArcher/rfPermute}
-  \item Report bugs at \url{https://github.com/EricArcher/rfPermute/issues}
+  \item \url{https://github.com/SWFSC/rfPermute}
+  \item Report bugs at \url{https://github.com/SWFSC/rfPermute/issues}
 }
 
 }
 \author{
-\strong{Maintainer}: Eric Archer \email{eric.archer@noaa.gov}
+\strong{Maintainer}: Eric Archer \email{eric.ivan.archer@gmail.com}
 
 }
 \keyword{internal}
diff --git a/man/rfROC.Rd b/man/rfROC.Rd
new file mode 100644
index 0000000..19c2b90
--- /dev/null
+++ b/man/rfROC.Rd
@@ -0,0 +1,39 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/rfROC.R
+\name{rfROC}
+\alias{rfROC}
+\title{Receiver Operator Curve}
+\usage{
+rfROC(x, plot = TRUE)
+}
+\arguments{
+\item{x}{a \code{rfPermute} or \code{randomForest} model object.}
+
+\item{plot}{logical? Plot the curve?}
+}
+\value{
+\describe{ a list returned invisbly containing:
+ \item{\code{roc}}{a list containing ROC results from \code{\link[pROC]{roc}} for each class}
+ \item{\code{smry}}{a data fame summarizing the threshold and AUC for each class}
+ \item{\code{plot}}{the \code{ggplot} plot of the ROC curves}
+}
+}
+\description{
+Create a Receiver Operator Curve (ROC) for each class in a 
+  Random Forest model and (optionally) plot them.
+}
+\examples{
+library(randomForest)
+data(mtcars)
+
+# random sampling with replacement
+rf <- randomForest(factor(am) ~ ., mtcars)
+
+roc <- rfROC(rf)
+
+roc$smry
+
+}
+\author{
+Eric Archer \email{eric.archer@noaa.gov}
+}
diff --git a/man/summary.Rd b/man/summary.Rd
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diff --git a/man/symb.metab.Rd b/man/symb.metab.Rd
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