diff --git a/pkg/caret/R/confusionMatrix.R b/pkg/caret/R/confusionMatrix.R
index 4c4ab151..bd012d6e 100644
--- a/pkg/caret/R/confusionMatrix.R
+++ b/pkg/caret/R/confusionMatrix.R
@@ -145,7 +145,7 @@ confusionMatrix.table <- function(data, positive = NULL, prevalence = NULL, ...)
     overall = overall, 
     byClass = tableStats,
     dots = list(...)), 
-            class = "confusionMatrix")
+    class = "confusionMatrix")
 }
 
 
@@ -170,17 +170,37 @@ as.matrix.confusionMatrix <- function(x, what = "xtabs", ...)
   out
 }
 
-as.table.confusionMatrix <- function(x, ...)  x$table
 
+as.table.confusionMatrix <- function(x, ...)  x$table
 
 
 confusionMatrix.train <- function(data, norm = "overall", dnn = c("Prediction", "Reference"), ...)
 {
-  if(data$modelType == "Regression") stop("confusion matrices are only valid for classification models")
-  if(!norm %in% c("none", "overall", "average")) stop("values for norm should be 'none', 'overall', 'byClass' or 'average'")
-  if(data$control$method %in% c("oob", "LOOCV", "none")) stop("cannot compute confusion matrices for leave-one-out, out-of-bag resampling or no resampling")
-  if(!is.null(data$control$index))
-  {
+  if (inherits(data, "train")) {
+    if(data$modelType == "Regression")
+      stop("confusion matrices are only valid for classification models")
+    if(data$control$method %in% c("oob", "LOOCV"))
+      stop("cannot compute confusion matrices for leave-one-out or out-of-bag resampling")
+    if(data$control$method == "none")
+      return(confusionMatrix(predict(data), data$trainingData$.outcome, dnn = dnn, ...))
+    
+    lev <- levels(data)
+    
+    ## get only best tune
+    names(data$bestTune) <- gsub("^\\.", "", names(data$bestTune))
+    resampledCM <- merge(data$bestTune, data$resampledCM)
+    
+  } else {
+    if(is.null(data$resampledCM))
+      stop("resampled confusion matrices are not availible")
+    if(data$control$method %in% c("oob", "LOOCV"))
+      stop("cannot compute confusion matrices for leave-one-out or out-of-bag resampling")
+    
+    lev <- data$obsLevels
+    resampledCM <- data$resampledCM
+  }
+  
+  if(!is.null(data$control$index)) {
     resampleN <- unlist(lapply(data$control$index, length))
     numResamp <- length(resampleN)
     resampText <- resampName(data)
@@ -189,81 +209,57 @@ confusionMatrix.train <- function(data, norm = "overall", dnn = c("Prediction",
     numResamp <- 0
   }
   
-  lev <- levels(data)
-  ## get only best tune
-  names(data$bestTune) <- gsub("^\\.", "", names(data$bestTune))
-  resampledCM <- merge(data$bestTune, data$resampledCM)
-  counts <- as.matrix(resampledCM[,grep("^cell", colnames(resampledCM))])
-  ## normalize by true class?
+  counts <- as.matrix(resampledCM[ , grep("^\\.?cell", colnames(resampledCM))])
+  
+  ## normalize?
+  norm <- match.arg(norm, c("none", "overall", "average"))
+  
+  if(norm == "none") counts <- matrix(apply(counts, 2, sum), nrow = length(lev))
+  else counts <- matrix(apply(counts, 2, mean), nrow = length(lev))
   
-  if(norm == "overall") counts <- t(apply(counts, 1, function(x)x/sum(x)))
-  if(norm == "average") counts <- counts/numResamp
-  overall <- matrix(apply(counts, 2, mean), nrow = length(lev))
-  rownames(overall) <- colnames(overall) <- lev
-  if(norm != "none") overall <- overall*100
-  names(dimnames(overall)) <- dnn
+  if(norm == "overall") counts <- counts / sum(counts) * 100
   
+  ## names
+  rownames(counts) <- colnames(counts) <- lev
+  names(dimnames(counts)) <- dnn
   
-  out <- list(table = as.table(overall),
+  ## out
+  out <- list(table = as.table(counts),
               norm = norm,
               B = length(data$control$index),
               text = paste(resampText, "Confusion Matrix"))
-  class(out) <- "confusionMatrix.train"
+  class(out) <- paste0("confusionMatrix.", class(data))
   out
 }
 
+confusionMatrix.rfe <- confusionMatrix.train
+confusionMatrix.sbf <- confusionMatrix.train
 
 print.confusionMatrix.train <- function(x, digits = 1, ...)
 {
   cat(x$text, "\n")
   normText <- switch(x$norm,
-                     none = "\n(entries are un-normalized counts)\n",
-                     average = "\n(entries are cell counts per resample)\n",
-                     overall = "\n(entries are percentages of table totals)\n",
-                     byClass = "\n(entries are percentages within the reference class)\n",
+                     none = "\n(entries are un-normalized aggregated counts)\n",
+                     average = "\n(entries are average cell counts across resamples)\n",
+                     overall = "\n(entries are percentual average cell counts across resamples)\n",
                      "")
   cat(normText, "\n")
-  if(x$norm == "none" & x$B == 1) print(getFromNamespace("confusionMatrix.table", "caret")(x$table)) else print(round(x$table, digits))
-  cat("\n")
-  invisible(x)
-}
-
-confusionMatrix.rfe <- function(data, norm = "overall", dnn = c("Prediction", "Reference"), ...)
-{
-  if(is.null(data$resampledCM)) stop("resampled confusion matrices are not availible")
-  if(!norm %in% c("none", "overall", "average")) stop("values for norm should be 'none', 'overall', 'byClass' or 'average'")
-  if(data$control$method %in% c("oob", "LOOCV")) stop("cannot compute confusion matrices for leave-one-out and out-of-bag resampling")
-  if(!is.null(data$control$index))
-  {
-    resampleN <- unlist(lapply(data$control$index, length))
-    numResamp <- length(resampleN)
-    resampText <- resampName(data)
+  if(x$norm == "none" & x$B == 1) {
+    print(getFromNamespace("confusionMatrix.table", "caret")(x$table)) 
   } else {
-    resampText <- ""
-    numResamp <- 0
+    print(round(x$table, digits))
+    
+    out <- cbind("Accuracy (average)", ":", formatC(sum(diag(x$table) / sum(x$table))))
+    
+    dimnames(out) <- list(rep("", nrow(out)), rep("", ncol(out)))
+    print(out, quote = FALSE)
+    cat("\n")
   }
-  
-  
-  resampledCM <- data$resampledCM
-  counts <- as.matrix(resampledCM[,grep("^\\.cell", colnames(resampledCM))])
-  ## normalize by true class?
-  
-  if(norm == "overall") counts <- t(apply(counts, 1, function(x)x/sum(x)))
-  if(norm == "average") counts <- counts/numResamp
-  overall <- matrix(apply(counts, 2, mean), nrow = length(data$obsLevels))
-  rownames(overall) <- colnames(overall) <- data$obsLevels
-  overall <- overall*100
-  names(dimnames(overall)) <- dnn
-  
-  
-  out <- list(table = overall,
-              norm = norm,
-              B = numResamp, 
-              text = paste(resampText, "Confusion Matrix"))
-  class(out) <- "confusionMatrix.rfe"
-  out
+  invisible(x)
 }
 
+print.confusionMatrix.rfe <- print.confusionMatrix.train
+print.confusionMatrix.sbf <- print.confusionMatrix.train
 
 resampName <- function(x, numbers = TRUE)
 {
@@ -288,10 +284,10 @@ resampName <- function(x, numbers = TRUE)
                   loocv = "Leave-One-Out Cross-Validation",
                   adaptive_boot = paste("Adaptively Bootstrapped (", numResamp, " reps)", sep = ""),
                   adaptive_cv = paste("Adaptively Cross-Validated (", x$control$number, " fold, repeated ",
-                                     x$control$repeats, " times)", sep = ""),
+                                      x$control$repeats, " times)", sep = ""),
                   adaptive_lgocv = paste("Adaptive Repeated Train/Test Splits Estimated (", numResamp, " reps, ",
-                                round(x$control$p, 2), "%)", sep = "")
-                  )
+                                         round(x$control$p, 2), "%)", sep = "")
+    )
   } else {
     out <- switch(tolower(x$control$method),
                   none = "None", 
@@ -309,45 +305,6 @@ resampName <- function(x, numbers = TRUE)
   out
 }
 
-confusionMatrix.sbf <- function(data, norm = "overall", dnn = c("Prediction", "Reference"), ...)
-{
-  if(is.null(data$resampledCM)) stop("resampled confusion matrices are not availible")
-  if(!norm %in% c("none", "overall", "average")) stop("values for norm should be 'none', 'overall', 'byClass' or 'average'")
-  if(data$control$method %in% c("oob", "LOOCV")) stop("cannot compute confusion matrices for leave-one-out and out-of-bag resampling")
-  if(!is.null(data$control$index))
-  {
-    resampleN <- unlist(lapply(data$control$index, length))
-    numResamp <- length(resampleN)
-    resampText <- resampName(data)
-  } else {
-    resampText <- ""
-    numResamp <- 0
-  }
-  
-  resampledCM <- data$resampledCM
-  counts <- as.matrix(resampledCM[,grep("^\\.cell", colnames(resampledCM))])
-  ## normalize by true class?
-  
-  if(norm == "overall") counts <- t(apply(counts, 1, function(x)x/sum(x)))
-  if(norm == "average") counts <- counts/numResamp
-  overall <- matrix(apply(counts, 2, mean), nrow = length(data$obsLevels))
-  rownames(overall) <- colnames(overall) <- data$obsLevels
-  overall <- overall*100
-  names(dimnames(overall)) <- dnn
-  
-  
-  out <- list(table = overall,
-              norm = norm,
-              B = numResamp,
-              text = paste(resampText, "Confusion Matrix"))
-  class(out) <- "confusionMatrix.sbf"
-  out
-}
-
-print.confusionMatrix.rfe <- print.confusionMatrix.train
-print.confusionMatrix.sbf <- print.confusionMatrix.train
-
-
 
 mcc <- function(tab, pos = colnames(tab)[1])
 {
diff --git a/pkg/caret/man/confusionMatrix.train.Rd b/pkg/caret/man/confusionMatrix.train.Rd
index d013360e..b2101d68 100644
--- a/pkg/caret/man/confusionMatrix.train.Rd
+++ b/pkg/caret/man/confusionMatrix.train.Rd
@@ -17,15 +17,15 @@ Using a \code{\link{train}}, \code{\link{rfe}}, \code{\link{sbf}} object, determ
 
 }
 \arguments{
-  \item{data}{an object of class \code{\link{train}}, \code{\link{rfe}}, \code{\link{sbf}} that did not use out-of-bag resampling or leave-one-out cross-validation.}
-  \item{norm}{a character string indicating how the table entries should be normalized. Valid values are "none", "overall" or "average". }
-  \item{dnn}{a character vector of dimnames for the table}
+  \item{data}{An object of class \code{\link{train}}, \code{\link{rfe}}, \code{\link{sbf}} that did not use out-of-bag resampling or leave-one-out cross-validation.}
+  \item{norm}{A character string indicating how the table entries should be normalized. Valid values are "none", "overall" or "average". }
+  \item{dnn}{A character vector of dimnames for the table}
   \item{\dots}{not used here}
 }
 \details{
 When \code{\link{train}} is used for tuning a model, it tracks the confusion matrix cell entries for the hold-out samples. These can be aggregated and used for diagnostic purposes. For \code{\link{train}}, the matrix is estimated for the final model tuning parameters determined by \code{\link{train}}. For \code{\link{rfe}}, the matrix is associated with the optimal number of variables.
 
-There are several ways to show the table entries. Using \code{norm = "none"} will show the frequencies of samples on each of the cells (across all resamples). \code{norm = "overall"} first divides the cell entries by the total number of data points in the table, then averages these percentages. \code{norm = "average"} takes the raw, aggregate cell counts across resamples and divides by the number of resamples (i.e. to yield an average count for each cell). 
+There are several ways to show the table entries. Using \code{norm = "none"} will show the aggregated counts of samples on each of the cells (across all resamples). For \code{norm = "average"}, the average number of cell counts across resamples is computed (this can help evaluate how many holdout samples there were on average). The default is \code{norm = "overall"}, which is equivalento to \code{"average"} but in percentages.
 }
 \value{
 a list of class \code{confusionMatrix.train}, \code{confusionMatrix.rfe} or \code{confusionMatrix.sbf} with elements
diff --git a/pkg/caret/man/trainControl.Rd b/pkg/caret/man/trainControl.Rd
index 0bd58d0b..f68581bc 100644
--- a/pkg/caret/man/trainControl.Rd
+++ b/pkg/caret/man/trainControl.Rd
@@ -77,11 +77,15 @@ Using adaptive resampling when \code{method} is either \code{"adaptive_cv"}, \co
 }
 
 The option \code{search = "grid"} uses the default grid search routine. When \code{search = "random"}, a random search procedure is used (Bergstra and Bengio, 2012). See \url{http://topepo.github.io/caret/random.html} for details and an example.
+
+The \code{"boot632"} method uses the 0.632 estimator presented in Efron (1983), not to be confused with the 0.632+ estimator proposed later by the same author.
 }
 
 \author{Max Kuhn}
 
 \references{
+Efron (1983). ``Estimating the error rate of a prediction rule: improvement on cross-validation''. Journal of the American Statistical Association, 78(382):316-331
+
 Bergstra and Bengio (2012), ``Random Search for Hyper-Parameter Optimization'', Journal of Machine Learning Research, 13(Feb):281-305
 
 Kuhn (2014), ``Futility Analysis in the Cross-Validation of Machine Learning Models'' \url{http://arxiv.org/abs/1405.6974},