diff --git a/pkg/caret/R/aaa.R b/pkg/caret/R/aaa.R
index 91367a9f..88bd0613 100644
--- a/pkg/caret/R/aaa.R
+++ b/pkg/caret/R/aaa.R
@@ -94,7 +94,7 @@ if(getRversion() >= "2.15.1"){
   ## nominalTrainWorkflow: no visible binding for global variable 'Resample'
   ## oobTrainWorkflow: no visible binding for global variable 'parm'
   ##
-  ##  result <- foreach(iter = seq(along = resampleIndex),
+  ##  result <- foreach(iter = seq(along.with = resampleIndex),
   ##                    .combine = "c", .verbose = FALSE,
   ##                    .packages = "caret", .errorhandling = "stop") %:%
   ##    foreach(parm = 1:nrow(info$loop), .combine = "c",
diff --git a/pkg/caret/R/adaptive.R b/pkg/caret/R/adaptive.R
index 22bbe565..b25f1658 100644
--- a/pkg/caret/R/adaptive.R
+++ b/pkg/caret/R/adaptive.R
@@ -22,12 +22,12 @@ adaptiveWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev,
   pkgs <- c("methods", "caret")
   if(!is.null(method$library)) pkgs <- c(pkgs, method$library)
 
-  init_index <- seq(along = resampleIndex)[1:(ctrl$adaptive$min-1)]
-  extra_index <- seq(along = resampleIndex)[-(1:(ctrl$adaptive$min-1))]
+  init_index <- seq(along.with = resampleIndex)[1:(ctrl$adaptive$min-1)]
+  extra_index <- seq(along.with = resampleIndex)[-(1:(ctrl$adaptive$min-1))]
 
   keep_pred <- isTRUE(ctrl$savePredictions) || ctrl$savePredictions %in% c("all", "final")
 
-  init_result <- foreach(iter = seq(along = init_index),
+  init_result <- foreach(iter = seq(along.with = init_index),
                          .combine = "c",
                          .verbose = FALSE,
                          .errorhandling = "stop") %:%
@@ -91,14 +91,14 @@ adaptiveWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev,
                   nPred <- length(holdoutIndex)
                   if(!is.null(lev)) {
                     predicted <- rep("", nPred)
-                    predicted[seq(along = predicted)] <- NA
+                    predicted[seq(along.with = predicted)] <- NA
                   } else {
                     predicted <- rep(NA, nPred)
                   }
                   if(!is.null(submod)) {
                     tmp <- predicted
                     predicted <- vector(mode = "list", length = nrow(info$submodels[[parm]]) + 1)
-                    for(i in seq(along = predicted)) predicted[[i]] <- tmp
+                    for(i in seq(along.with = predicted)) predicted[[i]] <- tmp
                     rm(tmp)
                   }
                 }
@@ -117,14 +117,14 @@ adaptiveWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev,
                 nPred <- length(holdoutIndex)
                 if(!is.null(lev)) {
                   predicted <- rep("", nPred)
-                  predicted[seq(along = predicted)] <- NA
+                  predicted[seq(along.with = predicted)] <- NA
                 } else {
                   predicted <- rep(NA, nPred)
                 }
                 if(!is.null(submod)) {
                   tmp <- predicted
                   predicted <- vector(mode = "list", length = nrow(info$submodels[[parm]]) + 1)
-                  for(i in seq(along = predicted)) predicted[[i]] <- tmp
+                  for(i in seq(along.with = predicted)) predicted[[i]] <- tmp
                   rm(tmp)
                 }
               }
@@ -145,7 +145,7 @@ adaptiveWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev,
                   {
                     tmp <- probValues
                     probValues <- vector(mode = "list", length = nrow(info$submodels[[parm]]) + 1)
-                    for(i in seq(along = probValues)) probValues[[i]] <- tmp
+                    for(i in seq(along.with = probValues)) probValues[[i]] <- tmp
                     rm(tmp)
                   }
                 }
@@ -175,12 +175,12 @@ adaptiveWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev,
 
                 ## same for the class probabilities
                 if(ctrl$classProbs) {
-                  for(k in seq(along = predicted)) predicted[[k]] <- cbind(predicted[[k]], probValues[[k]])
+                  for(k in seq(along.with = predicted)) predicted[[k]] <- cbind(predicted[[k]], probValues[[k]])
                 }
 
                 if(keep_pred) {
                   tmpPred <- predicted
-                  for(modIndex in seq(along = tmpPred))
+                  for(modIndex in seq(along.with = tmpPred))
                   {
                     tmpPred[[modIndex]]$rowIndex <- holdoutIndex
                     tmpPred[[modIndex]] <- merge(tmpPred[[modIndex]],
@@ -201,7 +201,7 @@ adaptiveWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev,
                 if(length(lev) > 1) {
                   cells <- lapply(predicted,
                                   function(x) flatTable(x$pred, x$obs))
-                  for(ind in seq(along = cells)) thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]])
+                  for(ind in seq(along.with = cells)) thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]])
                 }
                 thisResample <- do.call("rbind", thisResample)
                 thisResample <- cbind(allParam, thisResample)
@@ -315,14 +315,14 @@ adaptiveWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev,
                                      nPred <- length(holdoutIndex)
                                      if(!is.null(lev)) {
                                        predicted <- rep("", nPred)
-                                       predicted[seq(along = predicted)] <- NA
+                                       predicted[seq(along.with = predicted)] <- NA
                                      } else {
                                        predicted <- rep(NA, nPred)
                                      }
                                      if(!is.null(submod)) {
                                        tmp <- predicted
                                        predicted <- vector(mode = "list", length = nrow(new_info$submodels[[parm]]) + 1)
-                                       for(i in seq(along = predicted)) predicted[[i]] <- tmp
+                                       for(i in seq(along.with = predicted)) predicted[[i]] <- tmp
                                        rm(tmp)
                                      }
                                    }
@@ -341,14 +341,14 @@ adaptiveWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev,
                                    nPred <- length(holdoutIndex)
                                    if(!is.null(lev)) {
                                      predicted <- rep("", nPred)
-                                     predicted[seq(along = predicted)] <- NA
+                                     predicted[seq(along.with = predicted)] <- NA
                                    } else {
                                      predicted <- rep(NA, nPred)
                                    }
                                    if(!is.null(submod)) {
                                      tmp <- predicted
                                      predicted <- vector(mode = "list", length = nrow(new_info$submodels[[parm]]) + 1)
-                                     for(i in seq(along = predicted)) predicted[[i]] <- tmp
+                                     for(i in seq(along.with = predicted)) predicted[[i]] <- tmp
                                      rm(tmp)
                                    }
                                  }
@@ -369,7 +369,7 @@ adaptiveWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev,
                                      {
                                        tmp <- probValues
                                        probValues <- vector(mode = "list", length = nrow(new_info$submodels[[parm]]) + 1)
-                                       for(i in seq(along = probValues)) probValues[[i]] <- tmp
+                                       for(i in seq(along.with = probValues)) probValues[[i]] <- tmp
                                        rm(tmp)
                                      }
                                    }
@@ -400,12 +400,12 @@ adaptiveWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev,
 
                                    ## same for the class probabilities
                                    if(ctrl$classProbs) {
-                                     for(k in seq(along = predicted)) predicted[[k]] <- cbind(predicted[[k]], probValues[[k]])
+                                     for(k in seq(along.with = predicted)) predicted[[k]] <- cbind(predicted[[k]], probValues[[k]])
                                    }
 
                                    if(keep_pred) {
                                      tmpPred <- predicted
-                                     for(modIndex in seq(along = tmpPred))
+                                     for(modIndex in seq(along.with = tmpPred))
                                      {
                                        tmpPred[[modIndex]]$rowIndex <- holdoutIndex
                                        tmpPred[[modIndex]] <- merge(tmpPred[[modIndex]],
@@ -426,7 +426,7 @@ adaptiveWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev,
                                    if(length(lev) > 1) {
                                      cells <- lapply(predicted,
                                                      function(x) flatTable(x$pred, x$obs))
-                                     for(ind in seq(along = cells)) thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]])
+                                     for(ind in seq(along.with = cells)) thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]])
                                    }
                                    thisResample <- do.call("rbind", thisResample)
                                    thisResample <- cbind(allParam, thisResample)
@@ -541,7 +541,7 @@ adaptiveWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev,
     printed <- format(new_info$loop, digits = 4)
     colnames(printed) <- gsub("^\\.", "", colnames(printed))
 
-    final_index <- seq(along = resampleIndex)[(last_iter+1):length(ctrl$index)]
+    final_index <- seq(along.with = resampleIndex)[(last_iter+1):length(ctrl$index)]
     final_result <- foreach(iter = final_index,
                             .combine = "c",
                             .verbose = FALSE) %:%
@@ -604,14 +604,14 @@ adaptiveWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev,
                     nPred <- length(holdoutIndex)
                     if(!is.null(lev)) {
                       predicted <- rep("", nPred)
-                      predicted[seq(along = predicted)] <- NA
+                      predicted[seq(along.with = predicted)] <- NA
                     } else {
                       predicted <- rep(NA, nPred)
                     }
                     if(!is.null(submod)) {
                       tmp <- predicted
                       predicted <- vector(mode = "list", length = nrow(info$submodels[[parm]]) + 1)
-                      for(i in seq(along = predicted)) predicted[[i]] <- tmp
+                      for(i in seq(along.with = predicted)) predicted[[i]] <- tmp
                       rm(tmp)
                     }
                   }
@@ -630,14 +630,14 @@ adaptiveWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev,
                   nPred <- length(holdoutIndex)
                   if(!is.null(lev)) {
                     predicted <- rep("", nPred)
-                    predicted[seq(along = predicted)] <- NA
+                    predicted[seq(along.with = predicted)] <- NA
                   } else {
                     predicted <- rep(NA, nPred)
                   }
                   if(!is.null(submod)) {
                     tmp <- predicted
                     predicted <- vector(mode = "list", length = nrow(info$submodels[[parm]]) + 1)
-                    for(i in seq(along = predicted)) predicted[[i]] <- tmp
+                    for(i in seq(along.with = predicted)) predicted[[i]] <- tmp
                     rm(tmp)
                   }
                 }
@@ -658,7 +658,7 @@ adaptiveWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev,
                     {
                       tmp <- probValues
                       probValues <- vector(mode = "list", length = nrow(info$submodels[[parm]]) + 1)
-                      for(i in seq(along = probValues)) probValues[[i]] <- tmp
+                      for(i in seq(along.with = probValues)) probValues[[i]] <- tmp
                       rm(tmp)
                     }
                   }
@@ -689,12 +689,12 @@ adaptiveWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev,
 
                   ## same for the class probabilities
                   if(ctrl$classProbs) {
-                    for(k in seq(along = predicted)) predicted[[k]] <- cbind(predicted[[k]], probValues[[k]])
+                    for(k in seq(along.with = predicted)) predicted[[k]] <- cbind(predicted[[k]], probValues[[k]])
                   }
 
                   if(keep_pred) {
                     tmpPred <- predicted
-                    for(modIndex in seq(along = tmpPred))
+                    for(modIndex in seq(along.with = tmpPred))
                     {
                       tmpPred[[modIndex]]$rowIndex <- holdoutIndex
                       tmpPred[[modIndex]] <- merge(tmpPred[[modIndex]],
@@ -715,7 +715,7 @@ adaptiveWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev,
                   if(length(lev) > 1) {
                     cells <- lapply(predicted,
                                     function(x) flatTable(x$pred, x$obs))
-                    for(ind in seq(along = cells)) thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]])
+                    for(ind in seq(along.with = cells)) thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]])
                   }
                   thisResample <- do.call("rbind", thisResample)
                   thisResample <- cbind(allParam, thisResample)
diff --git a/pkg/caret/R/additive.R b/pkg/caret/R/additive.R
index 77461dc5..4232a71a 100644
--- a/pkg/caret/R/additive.R
+++ b/pkg/caret/R/additive.R
@@ -10,11 +10,11 @@ additivePlot <- function(x, data, n = 100, quant = 0, plot = TRUE, ...)
                    function(x, len, q) list(seq = seq(
                                               quantile(x, na.rm = TRUE, probs = q),
                                               quantile(x, na.rm = TRUE, probs = 1 - q),
-                                              length = len),
+                                              length.out = len),
                                             var = ""),
                    len = n,
                    q = quant)
-    for(i in seq(along = seqs)) seqs[[i]]$var <- colnames(data)[i]
+    for(i in seq(along.with = seqs)) seqs[[i]]$var <- colnames(data)[i]
     meds <- lapply(data,
                    function(x, len) rep(median(x, na.rm = TRUE), len),
                    len = n)
diff --git a/pkg/caret/R/avNNet.R b/pkg/caret/R/avNNet.R
index d2a1ec0e..5cfc2c92 100644
--- a/pkg/caret/R/avNNet.R
+++ b/pkg/caret/R/avNNet.R
@@ -113,7 +113,7 @@ avNNet.default <- function(x, y, repeats = 5,
     ## check for factors
     ## this is from nnet.formula
 
-    ind <- seq(along = y)
+    ind <- seq(along.with = y)
     if(is.factor(y))
       {
         classLev <- levels(y)
diff --git a/pkg/caret/R/bag.R b/pkg/caret/R/bag.R
index ba7fe470..4505bf86 100644
--- a/pkg/caret/R/bag.R
+++ b/pkg/caret/R/bag.R
@@ -133,7 +133,7 @@ bagControl <- function(
         {
           freaks <- table(subY)
           smallFreak <- min(freaks)
-          splitUp <- split(seq(along = subY), subY)
+          splitUp <- split(seq(along.with = subY), subY)
           splitUp <- lapply(splitUp,
                             sample,
                             size = smallFreak)
@@ -173,7 +173,7 @@ bagControl <- function(
   btSamples <- createResample(y, times = B)
 
   `%op%` <-  if(bagControl$allowParallel)  `%dopar%` else  `%do%`
-  btFits <- foreach(iter = seq(along = btSamples),
+  btFits <- foreach(iter = seq(along.with = btSamples),
                     .verbose = FALSE,
                     .packages = "caret",
                     .errorhandling = "stop") %op%
diff --git a/pkg/caret/R/bagEarth.R b/pkg/caret/R/bagEarth.R
index 5add1102..67b638a9 100644
--- a/pkg/caret/R/bagEarth.R
+++ b/pkg/caret/R/bagEarth.R
@@ -260,7 +260,7 @@
     requireNamespaceQuietStop("earth")
     ## get oob predictions
     getTrainPred <- function(x) {
-      oobIndex <- seq(along = x$fitted.values)
+      oobIndex <- seq(along.with = x$fitted.values)
       oobIndex <- oobIndex[!(oobIndex %in% unique(x$index))]
       data.frame(pred = x$fitted.values[oobIndex],
                  sample = oobIndex)
diff --git a/pkg/caret/R/bagFDA.R b/pkg/caret/R/bagFDA.R
index 1ec276e8..eac4270d 100644
--- a/pkg/caret/R/bagFDA.R
+++ b/pkg/caret/R/bagFDA.R
@@ -204,7 +204,7 @@ function(object, newdata = NULL, type = "class", ...)
      }
    pred <- rbind.fill(pred)
    out <- ddply(pred, .(sample),
-                function(x) colMeans(x[,seq(along = object$levels)], na.rm = TRUE))
+                function(x) colMeans(x[,seq(along.with = object$levels)], na.rm = TRUE))
    out <- out[,-1,drop = FALSE]
    rownames(out) <- rownames(newdata)
    predClass <- object$levels[apply(out, 1, which.max)]
diff --git a/pkg/caret/R/classDist.R b/pkg/caret/R/classDist.R
index 98ae1563..cbe1fd3d 100644
--- a/pkg/caret/R/classDist.R
+++ b/pkg/caret/R/classDist.R
@@ -75,7 +75,7 @@ classDist.default <- function(x, y, groups = 5,
   if(is.numeric(y))
     {
       y <- cut(y,
-               unique(quantile(y, probs = seq(0, 1, length = groups + 1))),
+               unique(quantile(y, probs = seq(0, 1, length.out = groups + 1))),
                include.lowest = TRUE)
       classLabels <- paste(round((1:groups)/groups*100, 2))
       y <- factor(y)
diff --git a/pkg/caret/R/classLevels.R b/pkg/caret/R/classLevels.R
index d2f2bb4b..62dfd86f 100644
--- a/pkg/caret/R/classLevels.R
+++ b/pkg/caret/R/classLevels.R
@@ -13,7 +13,7 @@ levels.train <- function(x, ...) {
       } else code <- x$modelInfo
       if(!is.null(code$levels)){
         checkInstall(code$library)
-        for(i in seq(along = code$library))
+        for(i in seq(along.with = code$library))
           do.call("requireNamespaceQuietStop", list(package = code$library[i]))
         out <- code$levels(x$finalModel, ...)
       } else out <- NULL
diff --git a/pkg/caret/R/confusionMatrix.R b/pkg/caret/R/confusionMatrix.R
index ceea8fc4..b75437c8 100644
--- a/pkg/caret/R/confusionMatrix.R
+++ b/pkg/caret/R/confusionMatrix.R
@@ -281,7 +281,7 @@ confusionMatrix.table <- function(data, positive = NULL,
 
     tableStats <- matrix(NA, nrow = length(classLevels), ncol = 11)
 
-    for(i in seq(along = classLevels)) {
+    for(i in seq(along.with = classLevels)) {
       pos <- classLevels[i]
       neg <- classLevels[!(classLevels %in% classLevels[i])]
       prev <- if(is.null(prevalence)) sum(data[, pos])/sum(data) else prevalence[pos]
diff --git a/pkg/caret/R/createDataPartition.R b/pkg/caret/R/createDataPartition.R
index 6d594a02..4eb78cf4 100644
--- a/pkg/caret/R/createDataPartition.R
+++ b/pkg/caret/R/createDataPartition.R
@@ -116,7 +116,7 @@ createDataPartition <- function (y, times = 1, p = 0.5, list = TRUE, groups = mi
 
   if(is.numeric(y)) {
     y <- cut(y,
-             unique(quantile(y, probs = seq(0, 1, length = groups))),
+             unique(quantile(y, probs = seq(0, 1, length.out = groups))),
              include.lowest = TRUE)
   } else {
     xtab <- table(y)
@@ -144,7 +144,7 @@ createDataPartition <- function (y, times = 1, p = 0.5, list = TRUE, groups = mi
   }
 
   for (j in 1:times) {
-    tmp <- dlply(data.frame(y = y, index = seq(along = y)),
+    tmp <- dlply(data.frame(y = y, index = seq(along.with = y)),
                  .(y), subsample, p = p)
     tmp <- sort(as.vector(unlist(tmp)))
     out[[j]] <- tmp
@@ -180,7 +180,7 @@ createDataPartition <- function (y, times = 1, p = 0.5, list = TRUE, groups = mi
       cuts <- floor(length(y)/k)
       if(cuts < 2) cuts <- 2
       if(cuts > 5) cuts <- 5
-      breaks <- unique(quantile(y, probs = seq(0, 1, length = cuts)))
+      breaks <- unique(quantile(y, probs = seq(0, 1, length.out = cuts)))
       y <- cut(y, breaks, include.lowest = TRUE)
     }
 
@@ -212,12 +212,12 @@ createDataPartition <- function (y, times = 1, p = 0.5, list = TRUE, groups = mi
           foldVector[which(y == names(numInClass)[i])] <- sample(1:k, size = numInClass[i])
         }
       }
-    } else foldVector <- seq(along = y)
+    } else foldVector <- seq(along.with = y)
 
     if(list) {
-      out <- split(seq(along = y), foldVector)
-      names(out) <- paste("Fold", gsub(" ", "0", format(seq(along = out))), sep = "")
-      if(returnTrain) out <- lapply(out, function(data, y) y[-data], y = seq(along = y))
+      out <- split(seq(along.with = y), foldVector)
+      names(out) <- paste("Fold", gsub(" ", "0", format(seq(along.with = out))), sep = "")
+      if(returnTrain) out <- lapply(out, function(data, y) y[-data], y = seq(along.with = y))
     } else out <- foldVector
     out
   }
@@ -230,7 +230,7 @@ createMultiFolds <- function(y, k = 10, times = 5) {
   for(i in 1:times) {
     tmp <- createFolds(y, k = k, list = TRUE, returnTrain = TRUE)
     names(tmp) <- paste("Fold",
-                        gsub(" ", "0", format(seq(along = tmp))),
+                        gsub(" ", "0", format(seq(along.with = tmp))),
                         ".",
                         prettyNums[i],
                         sep = "")
@@ -292,8 +292,8 @@ make_resamples <- function(ctrl_obj, outcome) {
     ctrl_obj$index <-
       switch(tolower(ctrl_obj$method),
              oob = NULL,
-             none = list(seq(along = outcome)),
-             apparent = list(all = seq(along = outcome)),
+             none = list(seq(along.with = outcome)),
+             apparent = list(all = seq(along.with = outcome)),
              alt_cv =, cv = createFolds(outcome, ctrl_obj$number, returnTrain = TRUE),
              repeatedcv =, adaptive_cv = createMultiFolds(outcome, ctrl_obj$number, ctrl_obj$repeats),
              loocv = createFolds(outcome, n, returnTrain = TRUE),
@@ -301,7 +301,7 @@ make_resamples <- function(ctrl_obj, outcome) {
              adaptive_boot = createResample(outcome, ctrl_obj$number),
              test = createDataPartition(outcome, 1, ctrl_obj$p),
              adaptive_lgocv =, lgocv = createDataPartition(outcome, ctrl_obj$number, ctrl_obj$p),
-             timeslice = createTimeSlices(seq(along = outcome),
+             timeslice = createTimeSlices(seq(along.with = outcome),
                                           initialWindow = ctrl_obj$initialWindow,
                                           horizon = ctrl_obj$horizon,
                                           fixedWindow = ctrl_obj$fixedWindow,
@@ -319,7 +319,7 @@ make_resamples <- function(ctrl_obj, outcome) {
   }
 
   if(ctrl_obj$method == "apparent")
-    ctrl_obj$indexOut <- list(all = seq(along = outcome))
+    ctrl_obj$indexOut <- list(all = seq(along.with = outcome))
 
   ## Create holdout indices
   if(is.null(ctrl_obj$indexOut) && ctrl_obj$method != "oob"){
@@ -328,7 +328,7 @@ make_resamples <- function(ctrl_obj, outcome) {
         if (inherits(outcome, "Surv"))
           1:nrow(outcome)
       else
-        seq(along = outcome)
+        seq(along.with = outcome)
       ctrl_obj$indexOut <-
         lapply(ctrl_obj$index, function(training)
           setdiff(y_index, training))
@@ -340,7 +340,7 @@ make_resamples <- function(ctrl_obj, outcome) {
       names(ctrl_obj$indexOut) <- prettySeq(ctrl_obj$indexOut)
     } else {
       ctrl_obj$indexOut <-
-        createTimeSlices(seq(along = outcome),
+        createTimeSlices(seq(along.with = outcome),
                          initialWindow = ctrl_obj$initialWindow,
                          horizon = ctrl_obj$horizon,
                          fixedWindow = ctrl_obj$fixedWindow,
diff --git a/pkg/caret/R/createResample.R b/pkg/caret/R/createResample.R
index 91ea02c8..3f855002 100644
--- a/pkg/caret/R/createResample.R
+++ b/pkg/caret/R/createResample.R
@@ -8,7 +8,7 @@ createResample <- function(y, times = 10, list = TRUE) {
   out <- apply(
     trainIndex, 2,
     function(data) {
-      index <- seq(along = data)
+      index <- seq(along.with = data)
       out <-
         sort(sample(index, size = length(index), replace = TRUE))
       out
diff --git a/pkg/caret/R/extractPrediction.R b/pkg/caret/R/extractPrediction.R
index 3712742d..a2a7d066 100644
--- a/pkg/caret/R/extractPrediction.R
+++ b/pkg/caret/R/extractPrediction.R
@@ -31,7 +31,7 @@ extractPrediction <- function(models,
     if(verbose) cat("There were ", sum(hasNa), "rows with missing values\n\n")
   }
   
-  for(i in seq(along = models))
+  for(i in seq(along.with = models))
   {
     if(!unkOnly)
     {
diff --git a/pkg/caret/R/extractProb.R b/pkg/caret/R/extractProb.R
index 8c13fb0c..0a70f26d 100644
--- a/pkg/caret/R/extractProb.R
+++ b/pkg/caret/R/extractProb.R
@@ -38,7 +38,7 @@ extractProb <- function(models,
     if(verbose) cat("There were ", sum(hasNa), "rows with missing values\n\n"); flush.console()
   }
 
-  for(i in seq(along = models))
+  for(i in seq(along.with = models))
   {
     if(verbose) cat("starting ", models[[i]]$method, "\n"); flush.console()
     if(!unkOnly) {
diff --git a/pkg/caret/R/featurePlot.R b/pkg/caret/R/featurePlot.R
index a2187e31..86b92220 100644
--- a/pkg/caret/R/featurePlot.R
+++ b/pkg/caret/R/featurePlot.R
@@ -66,7 +66,7 @@ function(x, y,
                lineInfo <-  trellis.par.get("superpose.line")
                pointInfo <-  trellis.par.get("superpose.symbol")
                uniqueGroups <- sort(unique(groups))
-               for (i in seq(along=uniqueGroups))
+               for (i in seq(along.with=uniqueGroups))
                {
                   id <- which(groups[subscripts] == uniqueGroups[i])
                   panel.xyplot(x[id], y[id], pch = pointInfo$pch[i],
diff --git a/pkg/caret/R/gafs.R b/pkg/caret/R/gafs.R
index c41afcfb..96bcc76d 100644
--- a/pkg/caret/R/gafs.R
+++ b/pkg/caret/R/gafs.R
@@ -136,7 +136,7 @@ ga_func_check <- function(x) {
 #' @export gafs_initial
 gafs_initial <- function (vars, popSize, ...)  {
   x <- matrix(NA, nrow = popSize, ncol = vars)
-  probs <- seq(.9, .1, length = popSize)
+  probs <- seq(.9, .1, length.out = popSize)
   for(i in 1:popSize){
     x[i,] <- sample(0:1, replace = TRUE,
                     size = vars,
@@ -997,7 +997,7 @@ gafs <- function (x, ...) UseMethod("gafs")
       gafsControl$indexOut <-
         lapply(gafsControl$index,
                function(training, allSamples) allSamples[-unique(training)],
-               allSamples = seq(along = y)
+               allSamples = seq(along.with = y)
                )
       names(gafsControl$indexOut) <-
         getFromNamespace("prettySeq", "caret")(gafsControl$indexOut)
@@ -1017,7 +1017,7 @@ gafs <- function (x, ...) UseMethod("gafs")
                              obs = sample(y, min(10, length(y))))
 
     if(is.factor(y))
-      for(i in seq(along = classLevels))
+      for(i in seq(along.with = classLevels))
         testOutput[, classLevels[i]] <- runif(nrow(testOutput))
 
     test <- gafsControl$functions$fitness_extern(testOutput, lev = classLevels)
@@ -1041,7 +1041,7 @@ gafs <- function (x, ...) UseMethod("gafs")
 
     result <-
       foreach(
-        i = seq(along = gafsControl$index),
+        i = seq(along.with = gafsControl$index),
         .combine = "c", .verbose = FALSE,
         .errorhandling = "stop") %op% {
       ga_select(
@@ -1094,9 +1094,9 @@ gafs <- function (x, ...) UseMethod("gafs")
       in_holdout <- createDataPartition(y,
                                         p = gafsControl$holdout,
                                         list = FALSE)
-      in_model <- seq(along = y)[-unique(in_holdout)]
+      in_model <- seq(along.with = y)[-unique(in_holdout)]
     } else {
-      in_model <- seq(along = y)
+      in_model <- seq(along.with = y)
       in_holdout <- NULL
     }
     final_ga <- ga_select(
@@ -1518,7 +1518,7 @@ update.gafs <- function(object, iter, x, y, ...) {
       gafsControl$indexOut <-
         lapply(gafsControl$index,
                function(training, allSamples) allSamples[-unique(training)],
-               allSamples = seq(along = y)
+               allSamples = seq(along.with = y)
         )
       names(gafsControl$indexOut) <-
         getFromNamespace("prettySeq", "caret")(gafsControl$indexOut)
@@ -1538,7 +1538,7 @@ update.gafs <- function(object, iter, x, y, ...) {
                              obs = sample(y, min(10, length(y))))
 
     if(is.factor(y))
-      for(i in seq(along = classLevels))
+      for(i in seq(along.with = classLevels))
         testOutput[, classLevels[i]] <- runif(nrow(testOutput))
     if(!is.null(perf_data))
       testOutput <- cbind(
@@ -1567,7 +1567,7 @@ update.gafs <- function(object, iter, x, y, ...) {
 
     result <-
       foreach(
-        i = seq(along = gafsControl$index),
+        i = seq(along.with = gafsControl$index),
         .combine = "c", .verbose = FALSE,
         .errorhandling = "stop") %op% {
           ga_select(
@@ -1622,9 +1622,9 @@ update.gafs <- function(object, iter, x, y, ...) {
       in_holdout <- createDataPartition(y,
                                         p = gafsControl$holdout,
                                         list = FALSE)
-      in_model <- seq(along = y)[-unique(in_holdout)]
+      in_model <- seq(along.with = y)[-unique(in_holdout)]
     } else {
-      in_model <- seq(along = y)
+      in_model <- seq(along.with = y)
       in_holdout <- NULL
     }
     final_ga <- ga_select(
diff --git a/pkg/caret/R/ggplot.R b/pkg/caret/R/ggplot.R
index 1e239ae0..4516a611 100644
--- a/pkg/caret/R/ggplot.R
+++ b/pkg/caret/R/ggplot.R
@@ -181,7 +181,7 @@ random_search_plot <- function(x, metric = x$metric[1]) {
   p_names <- as.character(params$parameter)
 
   exclude <- NULL
-  for(i in seq(along = p_names)) {
+  for(i in seq(along.with = p_names)) {
     if(all(is.na(x$results[, p_names[i]])))
       exclude <- c(exclude, i)
   }
diff --git a/pkg/caret/R/heldout.R b/pkg/caret/R/heldout.R
index ebf0b042..f4c589bf 100644
--- a/pkg/caret/R/heldout.R
+++ b/pkg/caret/R/heldout.R
@@ -106,7 +106,7 @@ oob_pred.list <- function(x, direction = "wide", what = "both", ...) {
 
   nms <- names(oob)
   if(is.null(nms)) nms <- well_numbered("Model", length(oob))
-  for(i in seq(along = nms)) oob[[i]]$.label <- nms[i]
+  for(i in seq(along.with = nms)) oob[[i]]$.label <- nms[i]
   oob <- rbind.fill(oob)
   if(length(table(table(oob$n))) > 1)
     stop("Some averages have different sample sizes than others")
diff --git a/pkg/caret/R/learning_curve.R b/pkg/caret/R/learning_curve.R
index 64fbd6d2..2852ef96 100644
--- a/pkg/caret/R/learning_curve.R
+++ b/pkg/caret/R/learning_curve.R
@@ -79,7 +79,7 @@ learning_curve_dat <- function(dat,
   resampled <- vector(mode = "list", length = n_size)
   tested <- if(test_prop > 0) resampled else NULL
   apparent <- resampled
-  for(i in seq(along = proportion)) {
+  for(i in seq(along.with = proportion)) {
     if(verbose) cat("Training for ", round(proportion[i]*100, 1),
                     "% (n = ", floor(n*proportion[i]), ")\n", sep = "")
     in_mod <- if(proportion[i] < 1) sample(for_model, size = floor(n*proportion[i])) else for_model
diff --git a/pkg/caret/R/lift.R b/pkg/caret/R/lift.R
index 6f2cd1dd..e397750b 100644
--- a/pkg/caret/R/lift.R
+++ b/pkg/caret/R/lift.R
@@ -162,7 +162,7 @@ lift.formula <- function(x, data = NULL,
   if(!is.null(labels)) {
     plotData$originalName <- plotData$liftModelVar
     plotData$liftModelVar <- as.character(plotData$liftModelVar)
-    for(i in seq(along = labels)) plotData$liftModelVar[plotData$liftModelVar == names(labels)[i]] <- labels[i]
+    for(i in seq(along.with = labels)) plotData$liftModelVar[plotData$liftModelVar == names(labels)[i]] <- labels[i]
     plotData$liftModelVar <- factor(plotData$liftModelVar,
                                     levels = labels)
   }
@@ -233,7 +233,7 @@ liftCalc <- function(x, class = levels(x$liftClassVar)[1], cuts = NULL) {
   baseline <- mean(x$liftClassVar == class)
   if(!is.null(cuts)) {
     if(length(cuts) == 1) {
-      cuts <- rev(seq(0, 1, length = cuts))
+      cuts <- rev(seq(0, 1, length.out = cuts))
     } else {
       cuts <- unique(c(1, sort(cuts, decreasing = TRUE), 0))
     }
@@ -249,7 +249,7 @@ liftCalc <- function(x, class = levels(x$liftClassVar)[1], cuts = NULL) {
                     n = NA,
                     Sn = NA,
                     Sp = NA)
-  for(i in seq(along = cuts)) {
+  for(i in seq(along.with = cuts)) {
     sub <- x$liftClassVar[x$liftProbVar >= tmp$cuts[i]]
     tmp$n[i] <- length(sub)
     tmp$events[i] <- sum(sub == class)
@@ -342,7 +342,7 @@ panel.lift2 <- function (x, y, pct = 0, values = NULL, ...)  {
     if(any(names(theDots) == "groups")) {
       dat <- data.frame(x = x, y = y, groups = theDots$groups)
       ung <- unique(dat$groups)
-      for(i in seq(along = ung))  {
+      for(i in seq(along.with = ung))  {
         dat0 <- subset(dat, groups == ung[i])
         plotRef(dat0$x, dat0$y, values, iter = i)
       }
@@ -464,7 +464,7 @@ get_ref_point <- function(dat, v, window = 5) {
   res <- data.frame(CumEventPct = v,
                     CumTestedPct = NA)
 
-  for(i in seq(along = v)) {
+  for(i in seq(along.with = v)) {
     nearest <- which.min((y - v[i])^2)
     index <- max(1, nearest - window):min(length(y), nearest + window)
     res$CumTestedPct[i] <-
diff --git a/pkg/caret/R/maxDissim.R b/pkg/caret/R/maxDissim.R
index 4baf18fc..3e23c2fc 100644
--- a/pkg/caret/R/maxDissim.R
+++ b/pkg/caret/R/maxDissim.R
@@ -61,7 +61,7 @@
 #'        xlab = "variable 1", ylab = "variable 2")
 #'   points(base, pch = 16, cex = .7)
 #'   
-#'   for(i in seq(along = newSamp))
+#'   for(i in seq(along.with = newSamp))
 #'     points(
 #'            pool[newSamp[i],1], 
 #'            pool[newSamp[i],2], 
@@ -167,11 +167,11 @@ splitByDissim <- function(x, p = .8, y = NULL, start = NULL, ...)
         if(!is.factor(y)) y <- as.factor(y)
         lvl <- levels(y)
         
-        ind <- split(seq(along = y), y)
-        ind2 <- lapply(ind, function(x) seq(along = x))
+        ind <- split(seq(along.with = y), y)
+        ind2 <- lapply(ind, function(x) seq(along.with = x))
         start2 <- lapply(ind, function(x, start) which(x %in% start),
                          start = start)
-        for(i in seq(along = lvl))
+        for(i in seq(along.with = lvl))
           {
             tmp <- splitter(x[ind[[i]],, drop = FALSE],
                             p = p,
diff --git a/pkg/caret/R/misc.R b/pkg/caret/R/misc.R
index 6e752bcf..6e5c1f22 100644
--- a/pkg/caret/R/misc.R
+++ b/pkg/caret/R/misc.R
@@ -1,5 +1,5 @@
 subsemble_index <- function(y, J = 2, V = 10){
-  dat <- data.frame(y = y, index = seq(along = y))
+  dat <- data.frame(y = y, index = seq(along.with = y))
   outer_index <- sample(1:J, size = nrow(dat), replace = TRUE)
   outer_splits <- vector(mode = "list", length = J)
   for(i in 1:J) {
@@ -15,7 +15,7 @@ subsemble_index <- function(y, J = 2, V = 10){
   }
   all_index <- lapply(outer_splits, foo, V = V)
   model_index <- holdout_index <- NULL
-  for(i in seq(along = all_index)) {
+  for(i in seq(along.with = all_index)) {
     model_index   <- c(model_index,   all_index[[i]]$model)
     holdout_index <- c(holdout_index, all_index[[i]]$holdout)
   }
@@ -57,7 +57,7 @@ evalSummaryFunction <- function(y, wts = NULL, perf = NULL, ctrl, lev, metric, m
   }
 
   if(ctrl$classProbs) {
-    for(i in seq(along = lev)) testOutput[, lev[i]] <- runif(nrow(testOutput))
+    for(i in seq(along.with = lev)) testOutput[, lev[i]] <- runif(nrow(testOutput))
     testOutput[, lev] <- t(apply(testOutput[, lev], 1, function(x) x/sum(x)))
   } else {
     if(metric == "ROC" & !ctrl$classProbs)
@@ -145,12 +145,12 @@ flatTable <- function(pred, obs)
 {
   cells <- as.vector(table(pred, obs))
   if(length(cells) == 0) cells <- rep(NA, length(levels(obs))^2)
-  names(cells) <- paste(".cell", seq(along= cells), sep = "")
+  names(cells) <- paste(".cell", seq(along.with= cells), sep = "")
   cells
 }
 
 
-prettySeq <- function(x) paste("Resample", gsub(" ", "0", format(seq(along = x))), sep = "")
+prettySeq <- function(x) paste("Resample", gsub(" ", "0", format(seq(along.with = x))), sep = "")
 
 #' @rdname caret-internal
 #' @export
@@ -196,12 +196,12 @@ partRuleSummary <- function(x)
   classPred <- grep("\\)$", conditions, value = TRUE)
   varUsage <- data.frame(Var = predictors,
                          Overall = 0)
-  for(i in seq(along = predictors))
+  for(i in seq(along.with = predictors))
     varUsage$Overall[i] <- sum(grepl(paste("^", predictors[i], sep = ""), conditions))
 
   numClass <- rep(NA, length(classes))
   names(numClass) <- classes
-  for(i in seq(along = classes))
+  for(i in seq(along.with = classes))
     numClass[i] <- sum(grepl(paste(":", classes[i], sep = " "), classPred))
 
   list(varUsage = varUsage,
@@ -222,12 +222,12 @@ ripperRuleSummary <- function(x)
   conditions <- grep("(<=|>=|<|>|=)", rules, value = TRUE)
   varUsage <- data.frame(Var = predictors,
                          Overall = 0)
-  for(i in seq(along = predictors))
+  for(i in seq(along.with = predictors))
     varUsage$Overall[i] <- sum(grepl(paste("\\(", predictors[i], sep = ""), conditions))
 
   numClass <- rep(NA, length(classes))
   names(numClass) <- classes
-  for(i in seq(along = classes))
+  for(i in seq(along.with = classes))
     numClass[i] <- sum(grepl(paste(x$terms[[2]], "=", classes[i], sep = ""), conditions))
 
   list(varUsage = varUsage,
@@ -255,7 +255,7 @@ repList <- function(x, times = 3, addIndex = FALSE)
 {
   out <- vector(mode = "list", length = times)
   out <- lapply(out, function(a, b) b, b = x)
-  if(addIndex) for(i in seq(along = out)) out[[i]]$.index <- i
+  if(addIndex) for(i in seq(along.with = out)) out[[i]]$.index <- i
   out
 }
 
@@ -400,10 +400,10 @@ var_seq <- function(p, classification = FALSE, len = 3) {
   } else {
     if(p <= len)
     {
-      tuneSeq <- floor(seq(2, to = p, length = p))
+      tuneSeq <- floor(seq(2, to = p, length.out = p))
     } else {
-      if(p < 500 ) tuneSeq <- floor(seq(2, to = p, length = len))
-      else tuneSeq <- floor(2^seq(1, to = log(p, base = 2), length = len))
+      if(p < 500 ) tuneSeq <- floor(seq(2, to = p, length.out = len))
+      else tuneSeq <- floor(2^seq(1, to = log(p, base = 2), length.out = len))
     }
   }
   if(any(table(tuneSeq) > 1)) {
@@ -629,14 +629,14 @@ fill_failed_pred <- function(index, lev, submod){
   nPred <- length(index)
   if(!is.null(lev)) {
     predicted <- rep("", nPred)
-    predicted[seq(along = predicted)] <- NA
+    predicted[seq(along.with = predicted)] <- NA
   } else {
     predicted <- rep(NA, nPred)
   }
   if(!is.null(submod)) {
     tmp <- predicted
     predicted <- vector(mode = "list", length = nrow(submod) + 1)
-    for(i in seq(along = predicted)) predicted[[i]] <- tmp
+    for(i in seq(along.with = predicted)) predicted[[i]] <- tmp
     rm(tmp)
   }
   predicted
diff --git a/pkg/caret/R/modelLookup.R b/pkg/caret/R/modelLookup.R
index bc098a8f..bcbb1e93 100644
--- a/pkg/caret/R/modelLookup.R
+++ b/pkg/caret/R/modelLookup.R
@@ -71,7 +71,7 @@ modelLookup <- function(model = NULL){
                   out$probModel <- !is.null(x$prob)
                   out
                 })
-  for(i in seq(along = out)) out[[i]]$model <- names(models)[i]
+  for(i in seq(along.with = out)) out[[i]]$model <- names(models)[i]
   out <- do.call("rbind", out)
   rownames(out) <- NULL
   out <- out[, c('model', 'parameter', 'label', 'forReg', 'forClass', 'probModel')]
@@ -90,7 +90,7 @@ missing_packages <- function(mods = getModelInfo()) {
 #' @export
 checkInstall <- function(pkg){
   good <- rep(TRUE, length(pkg))
-  for(i in seq(along = pkg)){
+  for(i in seq(along.with = pkg)){
     tested <- try(find.package(pkg[i]), silent = TRUE)
     if (inherits(tested, "try-error")) good[i] <- FALSE
   }
diff --git a/pkg/caret/R/panel.needle.R b/pkg/caret/R/panel.needle.R
index c9862967..6b9b680d 100644
--- a/pkg/caret/R/panel.needle.R
+++ b/pkg/caret/R/panel.needle.R
@@ -43,7 +43,7 @@
       pch <- rep(pch, length(x))
       pch <- ifelse(x == 0, NA, pch)
       
-      for(i in seq(along=x)) lsegments(x[i], y[i], 0, y[i])
+      for(i in seq(along.with=x)) lsegments(x[i], y[i], 0, y[i])
       if (is.null(groups)) 
         panel.xyplot(x = x, y = y, col = col, pch = pch, ...)
       else panel.superpose(x = x, y = y, groups = groups, col = col, pch = pch, ...)        
diff --git a/pkg/caret/R/plsda.R b/pkg/caret/R/plsda.R
index 9ac89934..4186347f 100644
--- a/pkg/caret/R/plsda.R
+++ b/pkg/caret/R/plsda.R
@@ -58,7 +58,7 @@
 #' \dontrun{
 #' data(mdrr)
 #' set.seed(1)
-#' inTrain <- sample(seq(along = mdrrClass), 450)
+#' inTrain <- sample(seq(along.with = mdrrClass), 450)
 #'
 #' nzv <- nearZeroVar(mdrrDescr)
 #' filteredDescr <- mdrrDescr[, -nzv]
@@ -173,7 +173,7 @@ predict.plsda <- function(object, newdata = NULL, ncomp = NULL, type = "class",
 
     requireNamespaceQuietStop("klaR")
     tmp <- vector(mode = "list", length = length(ncomp))
-    for(i in seq(along = ncomp)) {
+    for(i in seq(along.with = ncomp)) {
       tmp[[i]] <- predict(object$probModel[[ ncomp[i] ]],
                           as.data.frame(tmpPred[,-length(object$obsLevels),i]), stringsAsFactors = TRUE)
     }
@@ -196,7 +196,7 @@ predict.plsda <- function(object, newdata = NULL, ncomp = NULL, type = "class",
                      rownames(tmp[[1]]$posterior),
                      colnames(tmp[[1]]$posterior),
                      paste("ncomp", ncomp, sep = "")))
-      for(i in seq(along = ncomp)) out[,,i] <- tmp[[i]]$posterior
+      for(i in seq(along.with = ncomp)) out[,,i] <- tmp[[i]]$posterior
     }
   }
   out
diff --git a/pkg/caret/R/preProcess.R b/pkg/caret/R/preProcess.R
index e168af1f..c45aa41d 100644
--- a/pkg/caret/R/preProcess.R
+++ b/pkg/caret/R/preProcess.R
@@ -337,7 +337,7 @@ preProcess.default <- function(x, method = c("center", "scale"),
       }
       # now apply to current data
       if(length(yj) > 0) {
-        for(i in seq(along = yj)) {
+        for(i in seq(along.with = yj)) {
           who <- names(yj)[i]
           x[,who] <- recipes::yj_transform(x[,who], yj[who])
         }
@@ -557,7 +557,7 @@ predict.preProcess <- function(object, newdata, ...) {
     lam <- get_yj_lambda(object$yj)
     lam <- lam[!is.na(lam)]
     if(length(lam) > 0) {
-      for(i in seq(along = lam)) {
+      for(i in seq(along.with = lam)) {
         who <- names(lam)[i]
         newdata[,who] <- recipes::yj_transform(newdata[,who], lam[who])
       }
@@ -565,7 +565,7 @@ predict.preProcess <- function(object, newdata, ...) {
   }
 
   if(!is.null(object$et)) {
-    for(i in seq(along = object$et)) {
+    for(i in seq(along.with = object$et)) {
       who <-  names(object$et)[i]
       newdata[,who] <- predict(object$et[[who]], newdata[,who])
     }
@@ -623,7 +623,7 @@ predict.preProcess <- function(object, newdata, ...) {
     missingVars <- names(missingVars)[missingVars]
     ## ipred's bagging procedure only allows for data frames
     if(!is.data.frame(hasMiss)) hasMiss <- as.data.frame(hasMiss, stringsAsFactors = TRUE)
-    for(i in seq(along = missingVars)) {
+    for(i in seq(along.with = missingVars)) {
       preds <- predict(object$bagImp[[missingVars[i]]]$model,
                        hasMiss[, !colnames(hasMiss) %in% missingVars[i], drop = FALSE])
 
diff --git a/pkg/caret/R/predict.PLS.R b/pkg/caret/R/predict.PLS.R
index 1224de33..e3cc4951 100644
--- a/pkg/caret/R/predict.PLS.R
+++ b/pkg/caret/R/predict.PLS.R
@@ -26,7 +26,7 @@ predict.PLS <- function(object, newdata,
    dnB[[1]] <- c("(Intercept)", dnB[[1]])
    BInt <- array(dim = dB, dimnames = dnB)
    BInt[-1, , ] <- B
-   for (i in seq(along = 1:ncomp)) BInt[1, , i] <- object$Ymeans - object$Xmeans %*% B[, , i]
+   for (i in seq(along.with = 1:ncomp)) BInt[1, , i] <- object$Ymeans - object$Xmeans %*% B[, , i]
    B <- BInt
    # stop
    
diff --git a/pkg/caret/R/predictors.R b/pkg/caret/R/predictors.R
index 99555c22..3396b8e7 100644
--- a/pkg/caret/R/predictors.R
+++ b/pkg/caret/R/predictors.R
@@ -37,7 +37,7 @@ predictors.train <- function(x, ...) {
   } else code <- x$modelInfo
   if(!is.null(code$predictors)){
     checkInstall(code$library)
-    for(i in seq(along = code$library))
+    for(i in seq(along.with = code$library))
       do.call("requireNamespaceQuietStop", list(package = code$library[i]))
     out <- code$predictors(x$finalModel, ...)
   } else {
@@ -56,7 +56,7 @@ predictors.default <- function(x, ...) {
   if(!is.null(code)) {
     if(!is.null(code$predictors)){
       checkInstall(code$library)
-      for(i in seq(along = code$library))
+      for(i in seq(along.with = code$library))
         do.call("requireNamespaceQuietStop", list(package = code$library[i]))
       out <- code$predictors(x, ...)
     } else {
@@ -91,7 +91,7 @@ hasTerms <- function(x)
 basicVars <- function(x, y)
   {
     hasVar <- rep(NA, length(x))
-    for(i in seq(along = x))
+    for(i in seq(along.with = x))
       hasVar[i] <- length(grep(x[i], y, fixed = TRUE)) > 0
     x[hasVar]
   }
diff --git a/pkg/caret/R/print.train.R b/pkg/caret/R/print.train.R
index cb0346f6..33b1dcb5 100644
--- a/pkg/caret/R/print.train.R
+++ b/pkg/caret/R/print.train.R
@@ -192,7 +192,7 @@ stringFunc <- function (x)  {
         numVals <- apply(tuneAcc[, params, drop = FALSE], 2, function(x) length(unique(x)))
         if(any(numVals < 2)) {
           constString <- NULL
-          for(i in seq(along = numVals)) {
+          for(i in seq(along.with = numVals)) {
             if(numVals[i] == 1)
               constString <- c(constString,
                                paste0("Tuning parameter '",
@@ -210,7 +210,7 @@ stringFunc <- function (x)  {
       colnames(tuneAcc)[colnames(tuneAcc) == ".B"] <- "Resamples"
       nms <- names(tuneAcc)[names(tuneAcc) %in% params]
       sort_args <- vector(mode = "list", length = length(nms))
-      for(i in seq(along = nms)) {
+      for(i in seq(along.with = nms)) {
         sort_args[[i]] <- tuneAcc[, nms[i]]
       }
       tune_ord <- do.call("order", sort_args)
diff --git a/pkg/caret/R/resamples.R b/pkg/caret/R/resamples.R
index b9ce2223..2d5c5583 100644
--- a/pkg/caret/R/resamples.R
+++ b/pkg/caret/R/resamples.R
@@ -142,7 +142,7 @@ resamples.default <- function(x, modelNames = names(x), ...) {
   }
 
   rs_values <- vector(mode = "list", length = length(x))
-  for(i in seq(along = x)) {
+  for(i in seq(along.with = x)) {
     if(class(x[[i]])[1] == "rfe" && x[[i]]$control$returnResamp == "all"){
       warning(paste0("'", modelNames[i], "' did not have 'returnResamp=\"final\"; the optimal subset is used"))
     }
@@ -170,7 +170,7 @@ resamples.default <- function(x, modelNames = names(x), ...) {
   rs_values <- lapply(rs_values,
                       function(x, n) x[,n,drop = FALSE],
                       n = c(pNames, "Resample"))
-  for(mod in seq(along = modelNames)) {
+  for(mod in seq(along.with = modelNames)) {
     names(rs_values[[mod]])[names(rs_values[[mod]]) %in% pNames] <-
       paste(modelNames[mod], names(rs_values[[mod]])[names(rs_values[[mod]]) %in% pNames], sep = "~")
     out <- if(mod == 1) rs_values[[mod]] else merge(out, rs_values[[mod]])
@@ -209,7 +209,7 @@ sort.resamples <- function(x, decreasing = FALSE, metric = x$metric[1], FUN = me
 summary.resamples <- function(object, metric = object$metrics, ...){
   vals <- object$values[, names(object$values) != "Resample", drop = FALSE]
   out <- vector(mode = "list", length = length(metric))
-  for(i in seq(along = metric)) {
+  for(i in seq(along.with = metric)) {
     tmpData <- vals[, grep(paste("~", metric[i], sep = ""), names(vals), fixed = TRUE), drop = FALSE]
 
     out[[i]] <- do.call("rbind", lapply(tmpData, function(x) summary(x)[1:6]))
@@ -418,14 +418,14 @@ plot.prcomp.resamples <- function(x, what = "scree", dims = max(2, ncol(x$rotati
          scree =
 {
   barchart(x$sdev ~ paste("PC",
-                          gsub(" ", "0", format(seq(along = x$sdev))),
+                          gsub(" ", "0", format(seq(along.with = x$sdev))),
                           sep = ""),
            ylab = "Standard Deviation", ...)
 },
 cumulative =
 {
   barchart(cumsum(x$sdev^2)/sum(x$sdev^2) ~ paste("PC",
-                                                  gsub(" ", "0", format(seq(along = x$sdev))),
+                                                  gsub(" ", "0", format(seq(along.with = x$sdev))),
                                                   sep = ""),
            ylab = "Culmulative Percent of Variance", ...)
 },
@@ -530,7 +530,7 @@ print.summary.resamples <- function(x, ...)
 
   cat("\n")
 
-  for(i in seq(along = x$statistics))
+  for(i in seq(along.with = x$statistics))
   {
     cat(names(x$statistics)[i], "\n")
     print(x$statistics[[i]])
@@ -700,7 +700,7 @@ xyplot.resamples <- function (x, data = NULL, what = "scatter", models = NULL, m
       lx <- as.numeric(lx[subscripts])
       ux <- as.numeric(ux[subscripts])
       gps <- unique(groups)
-      for(i in seq(along = gps))
+      for(i in seq(along.with = gps))
       {
         panel.arrows(lx[groups == gps[i]],
                      y[groups == gps[i]],
@@ -1122,7 +1122,7 @@ diff.resamples <- function(x,
   if(adjustment == "bonferroni") confLevel <- 1 - ((1 - confLevel)/ncomp)
   allStats <- allDif
 
-  for(h in seq(along = metric))
+  for(h in seq(along.with = metric))
   {
     index <- 0
     dif <- matrix(NA,
@@ -1131,9 +1131,9 @@ diff.resamples <- function(x,
     stat <- vector(mode = "list", length = choose(length(models), 2))
 
     colnames(dif) <- paste("tmp", 1:ncol(dif), sep = "")
-    for(i in seq(along = models))
+    for(i in seq(along.with = models))
     {
-      for(j in seq(along = models))
+      for(j in seq(along.with = models))
       {
         if(i < j)
         {
@@ -1228,13 +1228,13 @@ summary.diff.resamples <- function(object, digits = max(3, getOption("digits") -
   all <- vector(mode = "list", length = length(object$metric))
   names(all) <- object$metric
 
-  for(h in seq(along = object$metric))
+  for(h in seq(along.with = object$metric))
   {
     pvals <- matrix(NA, nrow = length(object$models), ncol = length(object$models))
     meanDiff <- pvals
     index <- 0
-    for(i in seq(along = object$models)) {
-      for(j in seq(along = object$models)) {
+    for(i in seq(along.with = object$models)) {
+      for(j in seq(along.with = object$models)) {
         if(i < j) {
           index <- index + 1
           meanDiff[i, j] <- object$statistics[[h]][index][[1]]$estimate
@@ -1243,8 +1243,8 @@ summary.diff.resamples <- function(object, digits = max(3, getOption("digits") -
     }
 
     index <- 0
-    for(i in seq(along = object$models)) {
-      for(j in seq(along = object$models)) {
+    for(i in seq(along.with = object$models)) {
+      for(j in seq(along.with = object$models)) {
         if(i < j) {
           index <- index + 1
           pvals[j, i] <- object$statistics[[h]][index][[1]]$p.value
@@ -1284,13 +1284,13 @@ levelplot.diff.resamples <- function(x, data = NULL, metric = x$metric[1], what
   all <- vector(mode = "list", length = length(x$metric))
   names(all) <- x$metric
 
-  for(h in seq(along = x$metric))
+  for(h in seq(along.with = x$metric))
   {
     temp <- matrix(NA, nrow = length(x$models), ncol = length( x$models))
     index <- 0
-    for(i in seq(along = x$models))
+    for(i in seq(along.with = x$models))
     {
-      for(j in seq(along = x$models))
+      for(j in seq(along.with = x$models))
       {
 
         if(i < j)
@@ -1342,7 +1342,7 @@ print.summary.diff.resamples <- function(x, ...)
       "Lower diagonal: p-value for H0: difference = 0\n\n",
       sep = "")
 
-  for(i in seq(along = x$table))
+  for(i in seq(along.with = x$table))
   {
     cat(names(x$table)[i], "\n")
     print(x$table[[i]], quote = FALSE)
@@ -1419,9 +1419,9 @@ dotplot.diff.resamples <- function(x, data = NULL, metric = x$metric[1], ...)
   plotData <- as.data.frame(matrix(NA, ncol = 3, nrow = ncol(x$difs[[metric]])), stringsAsFactors = TRUE)
   ## Get point and interval estimates on the differences
   index <- 0
-  for(i in seq(along = x$models))
+  for(i in seq(along.with = x$models))
   {
-    for(j in seq(along = x$models))
+    for(j in seq(along.with = x$models))
     {
 
       if(i < j)
@@ -1462,7 +1462,7 @@ dotplot.diff.resamples <- function(x, data = NULL, metric = x$metric[1], ...)
                          col = plotTheme$reference.line$col[1],
                          lty = plotTheme$reference.line$lty[1],
                          lwd = plotTheme$reference.line$lwd[1])
-            for(i in seq(along = upper$mod))
+            for(i in seq(along.with = upper$mod))
             {
               panel.segments(upper$x[i], upper$mod[i], lower$x[i], lower$mod[i],
                              col = plotTheme$plot.line$col[1],
diff --git a/pkg/caret/R/rfe.R b/pkg/caret/R/rfe.R
index 513031b6..1427b8de 100644
--- a/pkg/caret/R/rfe.R
+++ b/pkg/caret/R/rfe.R
@@ -196,7 +196,7 @@ rfe <- function (x, ...) UseMethod("rfe")
     if(is.null(rfeControl$indexOut)){
       rfeControl$indexOut <- lapply(rfeControl$index,
                                     function(training, allSamples) allSamples[-unique(training)],
-                                    allSamples = seq(along = y))
+                                    allSamples = seq(along.with = y))
       names(rfeControl$indexOut) <- prettySeq(rfeControl$indexOut)
     }
 
@@ -209,7 +209,7 @@ rfe <- function (x, ...) UseMethod("rfe")
 
     if(is.factor(y))
     {
-      for(i in seq(along = classLevels)) testOutput[, classLevels[i]] <- runif(nrow(testOutput))
+      for(i in seq(along.with = classLevels)) testOutput[, classLevels[i]] <- runif(nrow(testOutput))
     }
 
     test <- rfeControl$functions$summary(testOutput, lev = classLevels)
@@ -412,7 +412,7 @@ rfeIter <- function(x, y,
   sizeText <- format(sizeValues)
 
   finalVariables <- vector(length(sizeValues), mode = "list")
-  for(k in seq(along = sizeValues))
+  for(k in seq(along.with = sizeValues))
   {
     if(!any(is.na(seeds))) set.seed(seeds[k])
     if(rfeControl$verbose)
@@ -1448,7 +1448,7 @@ rfe_rec <- function(x, y, test_x, test_y, perf_dat,
   sizeText <- format(sizeValues)
 
   finalVariables <- vector(length(sizeValues), mode = "list")
-  for (k in seq(along = sizeValues)) {
+  for (k in seq(along.with = sizeValues)) {
     if (!any(is.na(seeds)))
       set.seed(seeds[k])
 
@@ -1640,7 +1640,7 @@ rfe_rec <- function(x, y, test_x, test_y, perf_dat,
       rfeControl$indexOut <- lapply(rfeControl$index,
                                     function(training, allSamples)
                                       allSamples[-unique(training)],
-                                    allSamples = seq(along = y_dat))
+                                    allSamples = seq(along.with = y_dat))
       names(rfeControl$indexOut) <- prettySeq(rfeControl$indexOut)
     }
 
@@ -1656,7 +1656,7 @@ rfe_rec <- function(x, y, test_x, test_y, perf_dat,
     testOutput <- data.frame(pred = sample(y_dat, min(10, length(y_dat))),
                              obs = sample(y_dat, min(10, length(y_dat))))
     if (is.factor(y_dat)) {
-      for (i in seq(along = classLevels))
+      for (i in seq(along.with = classLevels))
         testOutput[, classLevels[i]] <- runif(nrow(testOutput))
     }
     if(!is.null(perf_data))
@@ -1865,7 +1865,7 @@ rfe_rec_workflow <- function(rec, data, sizes, ctrl, lev, ...) {
   `%op%` <- getOper(ctrl$allowParallel && foreach::getDoParWorkers() > 1)
   result <-
     foreach(
-      iter = seq(along = resampleIndex),
+      iter = seq(along.with = resampleIndex),
       .combine = "c",
       .verbose = FALSE,
       .errorhandling = "stop",
@@ -1974,7 +1974,7 @@ rfe_rec_workflow <- function(rec, data, sizes, ctrl, lev, ...) {
         ## So, we need to find out how many set of predictions there are:
         nReps <- length(table(rfeResults$pred$Variables))
         rfeResults$pred$rowIndex <-
-          rep(seq(along = y)[unique(holdoutIndex)], nReps)
+          rep(seq(along.with = y)[unique(holdoutIndex)], nReps)
       }
 
       if (is.factor(y) && length(lev) <= 50) {
@@ -2029,7 +2029,7 @@ rfe_rec_workflow <- function(rec, data, sizes, ctrl, lev, ...) {
 
   if (ctrl$method %in% c("boot632")) {
     externPerf <- merge(externPerf, apparent)
-    for (p in seq(along = perfNames)) {
+    for (p in seq(along.with = perfNames)) {
       const <- 1 - exp(-1)
       externPerf[, perfNames[p]] <-
         (const * externPerf[, perfNames[p]]) +  ((1 - const) * externPerf[, paste(perfNames[p], "Apparent", sep = "")])
@@ -2048,7 +2048,7 @@ rfe_rec_loo <- function(rec, data, sizes, ctrl, lev, ...) {
   `%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1)
   result <-
     foreach(
-      iter = seq(along = resampleIndex),
+      iter = seq(along.with = resampleIndex),
       .combine = "c",
       .verbose = FALSE,
       .errorhandling = "stop",
diff --git a/pkg/caret/R/safs.R b/pkg/caret/R/safs.R
index e13764c4..ed63c348 100644
--- a/pkg/caret/R/safs.R
+++ b/pkg/caret/R/safs.R
@@ -533,7 +533,7 @@ safs <- function (x, ...) UseMethod("safs")
     if(is.null(safsControl$indexOut)){
       safsControl$indexOut <- lapply(safsControl$index,
                                      function(training, allSamples) allSamples[-unique(training)],
-                                     allSamples = seq(along = y))
+                                     allSamples = seq(along.with = y))
       names(safsControl$indexOut) <- getFromNamespace("prettySeq", "caret")(safsControl$indexOut)
     }
 
@@ -551,7 +551,7 @@ safs <- function (x, ...) UseMethod("safs")
                              obs = sample(y, min(10, length(y))))
 
     if(is.factor(y))
-      for(i in seq(along = classLevels)) testOutput[, classLevels[i]] <- runif(nrow(testOutput))
+      for(i in seq(along.with = classLevels)) testOutput[, classLevels[i]] <- runif(nrow(testOutput))
 
     test <- safsControl$functions$fitness_extern(testOutput, lev = classLevels)
     perfNames <- names(test)
@@ -571,7 +571,7 @@ safs <- function (x, ...) UseMethod("safs")
 
     `%op%` <- getOper(safsControl$allowParallel && getDoParWorkers() > 1)
     #     sa_resampled <- external <- vector(mode = "list", length = length(safsControl$index))
-    result <- foreach(i = seq(along = safsControl$index), .combine = "c", .verbose = FALSE, .errorhandling = "stop") %op% {
+    result <- foreach(i = seq(along.with = safsControl$index), .combine = "c", .verbose = FALSE, .errorhandling = "stop") %op% {
       sa_select(x[safsControl$index[[i]],,drop=FALSE],
                 y[safsControl$index[[i]]],
                 funcs = safsControl$functions,
@@ -618,9 +618,9 @@ safs <- function (x, ...) UseMethod("safs")
       in_holdout <- createDataPartition(y,
                                         p = safsControl$holdout,
                                         list = FALSE)
-      in_model <- seq(along = y)[-unique(in_holdout)]
+      in_model <- seq(along.with = y)[-unique(in_holdout)]
     } else {
-      in_model <- seq(along = y)
+      in_model <- seq(along.with = y)
       in_holdout <- NULL
     }
     final_sa <- sa_select(x[in_model,,drop=FALSE],
@@ -812,7 +812,7 @@ safs_initial <- function (vars, prob = .20, ...)  {
 #' @export
 safs_perturb <- function(x, vars, number = floor(length(x)*.01) + 1) {
   bin <- index2vec(x, vars)
-  change <- sample(seq(along = bin), size = number)
+  change <- sample(seq(along.with = bin), size = number)
   bin[change] <- ifelse(bin[change] == 1, 0, 1)
   sort(which(bin == 1))
 }
@@ -1382,7 +1382,7 @@ update.safs <- function(object, iter, x, y, ...) {
       safsControl$indexOut <-
         lapply(safsControl$index,
                function(training, allSamples) allSamples[-unique(training)],
-               allSamples = seq(along = y))
+               allSamples = seq(along.with = y))
       names(safsControl$indexOut) <-
         getFromNamespace("prettySeq", "caret")(safsControl$indexOut)
     }
@@ -1401,7 +1401,7 @@ update.safs <- function(object, iter, x, y, ...) {
                              obs = sample(y, min(10, length(y))))
 
     if(is.factor(y))
-      for(i in seq(along = classLevels)) testOutput[, classLevels[i]] <- runif(nrow(testOutput))
+      for(i in seq(along.with = classLevels)) testOutput[, classLevels[i]] <- runif(nrow(testOutput))
     if(!is.null(perf_data))
       testOutput <- cbind(
         testOutput,
@@ -1427,7 +1427,7 @@ update.safs <- function(object, iter, x, y, ...) {
     `%op%` <- getOper(safsControl$allowParallel && getDoParWorkers() > 1)
 
     result <- foreach(
-      i = seq(along = safsControl$index),
+      i = seq(along.with = safsControl$index),
       .combine = "c",
       .verbose = FALSE,
       .errorhandling = "stop",
@@ -1510,9 +1510,9 @@ update.safs <- function(object, iter, x, y, ...) {
       in_holdout <- createDataPartition(y,
                                         p = safsControl$holdout,
                                         list = FALSE)
-      in_model <- seq(along = y)[-unique(in_holdout)]
+      in_model <- seq(along.with = y)[-unique(in_holdout)]
     } else {
-      in_model <- seq(along = y)
+      in_model <- seq(along.with = y)
       in_holdout <- NULL
     }
     final_sa <- sa_select(
diff --git a/pkg/caret/R/sampling.R b/pkg/caret/R/sampling.R
index 8cd9cbbf..c0290a26 100644
--- a/pkg/caret/R/sampling.R
+++ b/pkg/caret/R/sampling.R
@@ -48,7 +48,7 @@ downSample <- function(x, y, list = FALSE, yname = "Class") {
 
   x <- ddply(x, .(y),
              function(dat, n)
-               dat[sample(seq(along = dat$.outcome), n), , drop = FALSE],
+               dat[sample(seq(along.with = dat$.outcome), n), , drop = FALSE],
              n = minClass)
   y <- x$.outcome
   x <- x[, !(colnames(x) %in% c("y", ".outcome")), drop = FALSE]
diff --git a/pkg/caret/R/selectByFilter.R b/pkg/caret/R/selectByFilter.R
index 02e62826..1d0a99b7 100644
--- a/pkg/caret/R/selectByFilter.R
+++ b/pkg/caret/R/selectByFilter.R
@@ -196,7 +196,7 @@ sbf <- function (x, ...) UseMethod("sbf")
     if(is.null(sbfControl$indexOut)){
       sbfControl$indexOut <- lapply(sbfControl$index,
                                     function(training, allSamples) allSamples[-unique(training)],
-                                    allSamples = seq(along = y))
+                                    allSamples = seq(along.with = y))
       names(sbfControl$indexOut) <- prettySeq(sbfControl$indexOut)
     }
     ## check summary function and metric
@@ -204,7 +204,7 @@ sbf <- function (x, ...) UseMethod("sbf")
                              obs = sample(y, min(10, length(y))))
 
     if(is.factor(y))
-      for(i in seq(along = classLevels))
+      for(i in seq(along.with = classLevels))
         testOutput[, classLevels[i]] <- runif(nrow(testOutput))
 
 
@@ -389,7 +389,7 @@ sbf.formula <- function (form, data, ..., subset, na.action, contrasts = NULL) {
     if(is.null(sbfControl$indexOut)){
       sbfControl$indexOut <- lapply(sbfControl$index,
                                     function(training, allSamples) allSamples[-unique(training)],
-                                    allSamples = seq(along = y))
+                                    allSamples = seq(along.with = y))
       names(sbfControl$indexOut) <- prettySeq(sbfControl$indexOut)
     }
     ## check summary function and metric
@@ -397,7 +397,7 @@ sbf.formula <- function (form, data, ..., subset, na.action, contrasts = NULL) {
                              obs = sample(y, min(10, length(y))))
 
     if(is.factor(y))
-      for(i in seq(along = classLevels))
+      for(i in seq(along.with = classLevels))
         testOutput[, classLevels[i]] <- runif(nrow(testOutput))
     if(!is.null(perf_data))
       testOutput <- cbind(
@@ -522,7 +522,7 @@ sbf_rec <- function(rec, data, ctrl, lev, ...) {
 
   `%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1)
   result <- foreach(
-    iter = seq(along = resampleIndex),
+    iter = seq(along.with = resampleIndex),
     .combine = "c",
     .verbose = FALSE,
     .errorhandling = "stop",
@@ -612,7 +612,7 @@ sbf_rec <- function(rec, data, ctrl, lev, ...) {
 
     const <- 1-exp(-1)
 
-    for(p in seq(along = perfNames))
+    for(p in seq(along.with = perfNames))
       performance[perfNames[p]] <-
       (const * performance[perfNames[p]]) +  ((1-const) * apparent[perfNames[p]])
   }
@@ -637,7 +637,7 @@ sbf_loo_rec <- function(rec, data, ctrl, lev, ...) {
 
   `%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1)
   result <- foreach(
-    iter = seq(along = resampleIndex),
+    iter = seq(along.with = resampleIndex),
     .combine = "c",
     .verbose = FALSE,
     .errorhandling = "stop",
diff --git a/pkg/caret/R/sensitivity.R b/pkg/caret/R/sensitivity.R
index 1205dce3..7c68d383 100644
--- a/pkg/caret/R/sensitivity.R
+++ b/pkg/caret/R/sensitivity.R
@@ -90,7 +90,7 @@
 #'
 #' prev <- seq(0.001, .99, length = 20)
 #' npvVals <- ppvVals <- prev  * NA
-#' for(i in seq(along = prev))
+#' for(i in seq(along.with = prev))
 #'   {
 #'     ppvVals[i] <- posPredValue(pred, truth, prevalence = prev[i])
 #'     npvVals[i] <- negPredValue(pred, truth, prevalence = prev[i])
diff --git a/pkg/caret/R/sortImp.R b/pkg/caret/R/sortImp.R
index 268bfe03..36abdeb1 100644
--- a/pkg/caret/R/sortImp.R
+++ b/pkg/caret/R/sortImp.R
@@ -23,7 +23,7 @@ sortImp <- function(object, top)
 
    if(length(tiedRanks) > 0)
    {
-      for(i in seq(along = tiedRanks))
+      for(i in seq(along.with = tiedRanks))
       {
          tmp <- featureRank[featureRank == tiedRanks[i]] 
          featureRank[featureRank == tiedRanks[i]] <- tmp + runif(length(tmp), min = 0.001, max = 0.999)
diff --git a/pkg/caret/R/train.default.R b/pkg/caret/R/train.default.R
index ad2215ff..7c3ca134 100644
--- a/pkg/caret/R/train.default.R
+++ b/pkg/caret/R/train.default.R
@@ -345,7 +345,7 @@ train.default <- function(x, y,
       stop(paste("Model", method, "is not in caret's built-in library"), call. = FALSE)
   }
   checkInstall(models$library)
-  for(i in seq(along = models$library)) do.call("requireNamespaceQuietStop", list(package = models$library[i]))
+  for(i in seq(along.with = models$library)) do.call("requireNamespaceQuietStop", list(package = models$library[i]))
   if(any(names(models) == "check") && is.function(models$check)) {
     software_check <- models$check(models$library)
   }
@@ -585,7 +585,7 @@ train.default <- function(x, y,
     ##         tmp <- vector(mode = "list", length = nrow(param) + 1)
     ##         tmp[[1]] <- out
     ##
-    ##         for(j in seq(along = param$.n.trees))
+    ##         for(j in seq(along.with = param$.n.trees))
     ##           {
     ##             tmp[[j]]  <- predict(modelFit,
     ##                                  newdata,
@@ -816,7 +816,7 @@ train.default <- function(x, y,
 
   ## Reorder rows of performance
   orderList <- list()
-  for(i in seq(along = paramNames)) orderList[[i]] <- performance[,paramNames[i]]
+  for(i in seq(along.with = paramNames)) orderList[[i]] <- performance[,paramNames[i]]
 
   performance <- performance[do.call("order", orderList),]
 
@@ -831,7 +831,7 @@ train.default <- function(x, y,
 
   ## Make the final model based on the tuning results
 
-  indexFinal <- if(is.null(trControl$indexFinal)) seq(along = y) else trControl$indexFinal
+  indexFinal <- if(is.null(trControl$indexFinal)) seq(along.with = y) else trControl$indexFinal
 
   if(!(length(trControl$seeds) == 1 && is.na(trControl$seeds))) set.seed(trControl$seeds[[length(trControl$seeds)]][1])
   startFinalTime <- proc.time()
@@ -944,6 +944,7 @@ train.formula <- function (form, data, ..., weights, subset, na.action = na.fail
 
   # do we need the double colon here?
   m[[1]] <- quote(stats::model.frame)
+  names(m)[names(m) == "form"] <- "formula" # avoid warning under warnPartialMatchArgs=TRUE
   m <- eval.parent(m)
   if(nrow(m) < 1) stop("Every row has at least one missing value were found", call. = FALSE)
   Terms <- attr(m, "terms")
@@ -1012,7 +1013,7 @@ train.recipe <- function(x,
       stop(paste("Model", method, "is not in caret's built-in library"), call. = FALSE)
   }
   checkInstall(models$library)
-  for(i in seq(along = models$library))
+  for(i in seq(along.with = models$library))
     do.call("requireNamespace", list(package = models$library[i]))
   if(any(names(models) == "check") && is.function(models$check)) {
     software_check <- models$check(models$library)
@@ -1410,7 +1411,7 @@ train.recipe <- function(x,
 
   ## Reorder rows of performance
   orderList <- list()
-  for(i in seq(along = paramNames)) orderList[[i]] <- performance[,paramNames[i]]
+  for(i in seq(along.with = paramNames)) orderList[[i]] <- performance[,paramNames[i]]
 
   performance <- performance[do.call("order", orderList),]
 
@@ -1425,7 +1426,7 @@ train.recipe <- function(x,
 
   ## Make the final model based on the tuning results
   indexFinal <- if(is.null(trControl$indexFinal))
-    seq(along = data[[y_orig_val]]) else trControl$indexFinal
+    seq(along.with = data[[y_orig_val]]) else trControl$indexFinal
 
   if(!(length(trControl$seeds) == 1 && is.na(trControl$seeds)))
     set.seed(trControl$seeds[[length(trControl$seeds)]][1])
diff --git a/pkg/caret/R/train_recipes.R b/pkg/caret/R/train_recipes.R
index f192539b..201877aa 100644
--- a/pkg/caret/R/train_recipes.R
+++ b/pkg/caret/R/train_recipes.R
@@ -211,7 +211,7 @@ loo_train_rec <- function(rec, dat, info, method,
   if(!is.null(method$library))
     pkgs <- c(pkgs, method$library)
 
-  result <- foreach(iter = seq(along = ctrl$index),
+  result <- foreach(iter = seq(along.with = ctrl$index),
                     .combine = "rbind",
                     .verbose = FALSE,
                     .packages = pkgs,
@@ -309,7 +309,7 @@ loo_train_rec <- function(rec, dat, info, method,
                 if(testing) print(head(predicted))
                 ## same for the class probabilities
                 if(ctrl$classProbs) {
-                  for(k in seq(along = predicted)) predicted[[k]] <-
+                  for(k in seq(along.with = predicted)) predicted[[k]] <-
                       cbind(predicted[[k]], probValues[[k]])
                 }
                 predicted <- do.call("rbind", predicted)
@@ -409,7 +409,7 @@ train_rec <- function(rec, dat, info, method, ctrl, lev, testing = FALSE, ...) {
 
   export <- c()
 
-  result <- foreach(iter = seq(along = resampleIndex), .combine = "c", .packages = pkgs, .export = export) %:%
+  result <- foreach(iter = seq(along.with = resampleIndex), .combine = "c", .packages = pkgs, .export = export) %:%
     foreach(parm = 1L:nrow(info$loop), .combine = "c", .packages = pkgs, .export = export)  %op% {
 
       if(!(length(ctrl$seeds) == 1L && is.na(ctrl$seeds)))
@@ -517,7 +517,7 @@ train_rec <- function(rec, dat, info, method, ctrl, lev, testing = FALSE, ...) {
 
         if(keep_pred) {
           tmpPred <- predicted
-          for(modIndex in seq(along = tmpPred)) {
+          for(modIndex in seq(along.with = tmpPred)) {
             tmpPred[[modIndex]] <- merge(tmpPred[[modIndex]],
                                          allParam[modIndex,,drop = FALSE],
                                          all = TRUE)
@@ -537,7 +537,7 @@ train_rec <- function(rec, dat, info, method, ctrl, lev, testing = FALSE, ...) {
         if(length(lev) > 1 && length(lev) <= 50) {
           cells <- lapply(predicted,
                           function(x) flatTable(x$pred, x$obs))
-          for(ind in seq(along = cells))
+          for(ind in seq(along.with = cells))
             thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]])
         }
         thisResample <- do.call("rbind", thisResample)
@@ -663,12 +663,12 @@ train_adapt_rec <- function(rec, dat, info, method, ctrl, lev, metric, maximize,
   pkgs <- c("methods", "caret")
   if(!is.null(method$library)) pkgs <- c(pkgs, method$library)
 
-  init_index <- seq(along = resampleIndex)[1:(ctrl$adaptive$min-1)]
-  extra_index <- seq(along = resampleIndex)[-(1:(ctrl$adaptive$min-1))]
+  init_index <- seq(along.with = resampleIndex)[1:(ctrl$adaptive$min-1)]
+  extra_index <- seq(along.with = resampleIndex)[-(1:(ctrl$adaptive$min-1))]
 
   keep_pred <- isTRUE(ctrl$savePredictions) || ctrl$savePredictions %in% c("all", "final")
 
-  init_result <- foreach(iter = seq(along = init_index),
+  init_result <- foreach(iter = seq(along.with = init_index),
                          .combine = "c",
                          .verbose = FALSE,
                          .packages = pkgs,
@@ -776,13 +776,13 @@ train_adapt_rec <- function(rec, dat, info, method, ctrl, lev, metric, maximize,
 
                 ## same for the class probabilities
                 if(ctrl$classProbs) {
-                  for(k in seq(along = predicted))
+                  for(k in seq(along.with = predicted))
                     predicted[[k]] <- cbind(predicted[[k]], probValues[[k]])
                 }
 
                 if(keep_pred) {
                   tmpPred <- predicted
-                  for(modIndex in seq(along = tmpPred)) {
+                  for(modIndex in seq(along.with = tmpPred)) {
                     tmpPred[[modIndex]]$rowIndex <- holdoutIndex
                     tmpPred[[modIndex]] <- merge(tmpPred[[modIndex]],
                                                  allParam[modIndex,,drop = FALSE],
@@ -802,7 +802,7 @@ train_adapt_rec <- function(rec, dat, info, method, ctrl, lev, metric, maximize,
                 if(length(lev) > 1 && length(lev) <= 50) {
                   cells <- lapply(predicted,
                                   function(x) flatTable(x$pred, x$obs))
-                  for(ind in seq(along = cells)) thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]])
+                  for(ind in seq(along.with = cells)) thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]])
                 }
                 thisResample <- do.call("rbind", thisResample)
                 thisResample <- cbind(allParam, thisResample)
@@ -955,13 +955,13 @@ train_adapt_rec <- function(rec, dat, info, method, ctrl, lev, metric, maximize,
 
                     ## same for the class probabilities
                     if(ctrl$classProbs) {
-                      for(k in seq(along = predicted))
+                      for(k in seq(along.with = predicted))
                         predicted[[k]] <- cbind(predicted[[k]], probValues[[k]])
                     }
 
                     if(keep_pred) {
                       tmpPred <- predicted
-                      for(modIndex in seq(along = tmpPred)) {
+                      for(modIndex in seq(along.with = tmpPred)) {
                         tmpPred[[modIndex]]$rowIndex <- holdoutIndex
                         tmpPred[[modIndex]] <- merge(tmpPred[[modIndex]],
                                                      allParam[modIndex,,drop = FALSE],
@@ -981,7 +981,7 @@ train_adapt_rec <- function(rec, dat, info, method, ctrl, lev, metric, maximize,
                     if(length(lev) > 1 && length(lev) <= 50) {
                       cells <- lapply(predicted,
                                       function(x) flatTable(x$pred, x$obs))
-                      for(ind in seq(along = cells))
+                      for(ind in seq(along.with = cells))
                         thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]])
                     }
                     thisResample <- do.call("rbind", thisResample)
@@ -1093,7 +1093,7 @@ train_adapt_rec <- function(rec, dat, info, method, ctrl, lev, metric, maximize,
     printed <- format(new_info$loop, digits = 4)
     colnames(printed) <- gsub("^\\.", "", colnames(printed))
 
-    final_index <- seq(along = resampleIndex)[(last_iter+1):length(ctrl$index)]
+    final_index <- seq(along.with = resampleIndex)[(last_iter+1):length(ctrl$index)]
     final_result <- foreach(iter = final_index,
                             .combine = "c",
                             .verbose = FALSE,
@@ -1200,13 +1200,13 @@ train_adapt_rec <- function(rec, dat, info, method, ctrl, lev, metric, maximize,
 
                   ## same for the class probabilities
                   if(ctrl$classProbs) {
-                    for(k in seq(along = predicted))
+                    for(k in seq(along.with = predicted))
                       predicted[[k]] <- cbind(predicted[[k]], probValues[[k]])
                   }
 
                   if(keep_pred) {
                     tmpPred <- predicted
-                    for(modIndex in seq(along = tmpPred)) {
+                    for(modIndex in seq(along.with = tmpPred)) {
                       tmpPred[[modIndex]]$rowIndex <- holdoutIndex
                       tmpPred[[modIndex]] <- merge(tmpPred[[modIndex]],
                                                    allParam[modIndex,,drop = FALSE],
@@ -1226,7 +1226,7 @@ train_adapt_rec <- function(rec, dat, info, method, ctrl, lev, metric, maximize,
                   if(length(lev) > 1 && length(lev) <= 50) {
                     cells <- lapply(predicted,
                                     function(x) flatTable(x$pred, x$obs))
-                    for(ind in seq(along = cells))
+                    for(ind in seq(along.with = cells))
                       thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]])
                   }
                   thisResample <- do.call("rbind", thisResample)
diff --git a/pkg/caret/R/twoClassSim.R b/pkg/caret/R/twoClassSim.R
index b43cb304..a78a1bcf 100644
--- a/pkg/caret/R/twoClassSim.R
+++ b/pkg/caret/R/twoClassSim.R
@@ -202,9 +202,9 @@ twoClassSim <- function(n = 100,
     2*sin(pi*tmpData$Nonlinear2* tmpData$Nonlinear3) 
   
   if(linearVars > 0) {
-    lin <- seq(10, 1, length = linearVars)/4 
+    lin <- seq(10, 1, length.out = linearVars)/4 
     lin <- lin * rep(c(-1, 1), floor(linearVars)+1)[1:linearVars] 
-    for(i in seq(along = lin)) lp <- lp + tmpData[, i+3]*lin[i]
+    for(i in seq(along.with = lin)) lp <- lp + tmpData[, i+3]*lin[i]
   }
   
   if(ordinal){
diff --git a/pkg/caret/R/varImp.R b/pkg/caret/R/varImp.R
index 052747ef..da549a88 100644
--- a/pkg/caret/R/varImp.R
+++ b/pkg/caret/R/varImp.R
@@ -280,7 +280,7 @@ GarsonWeights_FCNN4R <- function (object, xnames = NULL, ynames = NULL) {
 varImpDependencies <- function(libName){
   code <- getModelInfo(libName, regex = FALSE)[[1]]
   checkInstall(code$library)
-  for(i in seq(along = code$library))
+  for(i in seq(along.with = code$library))
     do.call("requireNamespaceQuietStop", list(package = code$library[i]))
   return(code)
 }
diff --git a/pkg/caret/R/varImp.train.R b/pkg/caret/R/varImp.train.R
index 5db6db2b..70eea0de 100644
--- a/pkg/caret/R/varImp.train.R
+++ b/pkg/caret/R/varImp.train.R
@@ -6,7 +6,7 @@
   if(is.null(code$varImp)) useModel <- FALSE
   if(useModel) {
     checkInstall(code$library)
-    for(i in seq(along = code$library))
+    for(i in seq(along.with = code$library))
       do.call("requireNamespaceQuietStop", list(package = code$library[i]))
     imp <- code$varImp(object$finalModel, ...)
     modelName <- object$method
diff --git a/pkg/caret/R/workflows.R b/pkg/caret/R/workflows.R
index 059a096a..80bc3665 100644
--- a/pkg/caret/R/workflows.R
+++ b/pkg/caret/R/workflows.R
@@ -77,7 +77,7 @@ nominalTrainWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev, tes
   if(!is.null(method$library)) pkgs <- c(pkgs, method$library)
   export <- c()
 
-  result <- foreach(iter = seq(along = resampleIndex), .combine = "c", .verbose = FALSE, .export = export, .packages = "caret") %:%
+  result <- foreach(iter = seq(along.with = resampleIndex), .combine = "c", .verbose = FALSE, .export = export, .packages = "caret") %:%
     foreach(parm = 1L:nrow(info$loop), .combine = "c", .verbose = FALSE, .export = export , .packages = "caret")  %op%
     {
       if(!(length(ctrl$seeds) == 1 && is.na(ctrl$seeds))) set.seed(ctrl$seeds[[iter]][parm])
@@ -190,7 +190,7 @@ nominalTrainWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev, tes
 
         if(keep_pred) {
           tmpPred <- predicted
-          for(modIndex in seq(along = tmpPred)) {
+          for(modIndex in seq(along.with = tmpPred)) {
             tmpPred[[modIndex]] <- merge(tmpPred[[modIndex]],
                                          allParam[modIndex,,drop = FALSE],
                                          all = TRUE)
@@ -209,7 +209,7 @@ nominalTrainWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev, tes
         if(length(lev) > 1 && length(lev) <= 50) {
           cells <- lapply(predicted,
                           function(x) flatTable(x$pred, x$obs))
-          for(ind in seq(along = cells)) thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]])
+          for(ind in seq(along.with = cells)) thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]])
         }
         thisResample <- do.call("rbind", thisResample)
         thisResample <- cbind(allParam, thisResample)
@@ -347,7 +347,7 @@ looTrainWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev, testing
   pkgs <- c("methods", "caret")
   if(!is.null(method$library)) pkgs <- c(pkgs, method$library)
 
-  result <- foreach(iter = seq(along = ctrl$index), .combine = "rbind", .verbose = FALSE, .errorhandling = "stop", .packages = "caret") %:%
+  result <- foreach(iter = seq(along.with = ctrl$index), .combine = "rbind", .verbose = FALSE, .errorhandling = "stop", .packages = "caret") %:%
     foreach(parm = 1:nrow(info$loop), .combine = "rbind", .verbose = FALSE, .errorhandling = "stop", .packages = "caret") %op% {
 
       if(!(length(ctrl$seeds) == 1 && is.na(ctrl$seeds))) set.seed(ctrl$seeds[[iter]][parm])
@@ -432,12 +432,12 @@ looTrainWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev, testing
                             y = y[holdoutIndex],
                             wts = wts[holdoutIndex],
                             lv = lev,
-                            rows = seq(along = y)[holdoutIndex])
+                            rows = seq(along.with = y)[holdoutIndex])
         if(testing) print(head(predicted))
 
         ## same for the class probabilities
         if(ctrl$classProbs)
-          for(k in seq(along = predicted))
+          for(k in seq(along.with = predicted))
             predicted[[k]] <- cbind(predicted[[k]], probValues[[k]])
         predicted <- do.call("rbind", predicted)
         allParam <- expandParameters(info$loop[parm,,drop = FALSE], submod)
@@ -451,7 +451,7 @@ looTrainWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev, testing
                                  stringsAsFactors = FALSE)
         if(!is.null(wts)) predicted$weights <- wts[holdoutIndex]
         if(ctrl$classProbs) predicted <- cbind(predicted, probValues)
-        predicted$rowIndex <- seq(along = y)[holdoutIndex]
+        predicted$rowIndex <- seq(along.with = y)[holdoutIndex]
         predicted <- cbind(predicted, info$loop[parm,,drop = FALSE])
 
       }
@@ -525,7 +525,7 @@ nominalSbfWorkflow <- function(x, y, ppOpts, ctrl, lev, ...) {
 
   `%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1)
   result <- foreach(
-    iter = seq(along = resampleIndex),
+    iter = seq(along.with = resampleIndex),
     .combine = "c",
     .verbose = FALSE,
     .errorhandling = "stop",
@@ -552,7 +552,7 @@ nominalSbfWorkflow <- function(x, y, ppOpts, ctrl, lev, ...) {
     if(ctrl$saveDetails) {
       tmpPred <- sbfResults$pred
       tmpPred$Resample <- names(resampleIndex)[iter]
-      tmpPred$rowIndex <- seq(along = y)[unique(holdoutIndex)]
+      tmpPred$rowIndex <- seq(along.with = y)[unique(holdoutIndex)]
     } else tmpPred <- NULL
     resamples <- ctrl$functions$summary(sbfResults$pred, lev = lev)
     if(is.factor(y) && length(lev) <= 50)
@@ -582,7 +582,7 @@ nominalSbfWorkflow <- function(x, y, ppOpts, ctrl, lev, ...) {
 
     const <- 1-exp(-1)
 
-    for(p in seq(along = perfNames))
+    for(p in seq(along.with = perfNames))
       performance[perfNames[p]] <-
       (const * performance[perfNames[p]]) +  ((1-const) * apparent[perfNames[p]])
   }
@@ -609,7 +609,7 @@ looSbfWorkflow <- function(x, y, ppOpts, ctrl, lev, ...) {
 
   `%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1)
   result <- foreach(
-    iter = seq(along = resampleIndex),
+    iter = seq(along.with = resampleIndex),
     .combine = "c",
     .verbose = FALSE,
     .errorhandling = "stop",
@@ -662,7 +662,7 @@ nominalRfeWorkflow <- function(x, y, sizes, ppOpts, ctrl, lev, ...)
   }
 
   `%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1)
-  result <- foreach(iter = seq(along = resampleIndex), .combine = "c", .verbose = FALSE, .errorhandling = "stop", .packages = "caret") %op%
+  result <- foreach(iter = seq(along.with = resampleIndex), .combine = "c", .verbose = FALSE, .errorhandling = "stop", .packages = "caret") %op%
   {
     loadNamespace("caret")
     requireNamespace("plyr")
@@ -694,7 +694,7 @@ nominalRfeWorkflow <- function(x, y, sizes, ppOpts, ctrl, lev, ...)
       ## If the user did not have nrow(x) in 'sizes', rfeIter added it.
       ## So, we need to find out how many set of predictions there are:
       nReps <- length(table(rfeResults$pred$Variables))
-      rfeResults$pred$rowIndex <- rep(seq(along = y)[unique(holdoutIndex)], nReps)
+      rfeResults$pred$rowIndex <- rep(seq(along.with = y)[unique(holdoutIndex)], nReps)
     }
 
     if(is.factor(y) && length(lev) <= 50) {
@@ -730,7 +730,7 @@ nominalRfeWorkflow <- function(x, y, sizes, ppOpts, ctrl, lev, ...)
   if(ctrl$method %in% c("boot632"))
   {
     externPerf <- merge(externPerf, apparent)
-    for(p in seq(along = perfNames))
+    for(p in seq(along.with = perfNames))
     {
       const <- 1-exp(-1)
       externPerf[, perfNames[p]] <- (const * externPerf[, perfNames[p]]) +  ((1-const) * externPerf[, paste(perfNames[p],"Apparent", sep = "")])
@@ -749,7 +749,7 @@ looRfeWorkflow <- function(x, y, sizes, ppOpts, ctrl, lev, ...)
 
   resampleIndex <- ctrl$index
   `%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1)
-  result <- foreach(iter = seq(along = resampleIndex), .combine = "c", .verbose = FALSE, .errorhandling = "stop", .packages = "caret") %op%
+  result <- foreach(iter = seq(along.with = resampleIndex), .combine = "c", .verbose = FALSE, .errorhandling = "stop", .packages = "caret") %op%
   {
     loadNamespace("caret")
     requireNamespaceQuietStop("methods")
diff --git a/pkg/caret/man/maxDissim.Rd b/pkg/caret/man/maxDissim.Rd
index a83f98a2..76fb5848 100644
--- a/pkg/caret/man/maxDissim.Rd
+++ b/pkg/caret/man/maxDissim.Rd
@@ -89,7 +89,7 @@ example <- function(pct = 1, obj = minDiss, ...)
        xlab = "variable 1", ylab = "variable 2")
   points(base, pch = 16, cex = .7)
   
-  for(i in seq(along = newSamp))
+  for(i in seq(along.with = newSamp))
     points(
            pool[newSamp[i],1], 
            pool[newSamp[i],2], 
diff --git a/pkg/caret/man/plsda.Rd b/pkg/caret/man/plsda.Rd
index 18d40e5e..4ad1e72a 100644
--- a/pkg/caret/man/plsda.Rd
+++ b/pkg/caret/man/plsda.Rd
@@ -83,7 +83,7 @@ for the posterior probability calculations.
 \dontrun{
 data(mdrr)
 set.seed(1)
-inTrain <- sample(seq(along = mdrrClass), 450)
+inTrain <- sample(seq(along.with = mdrrClass), 450)
 
 nzv <- nearZeroVar(mdrrDescr)
 filteredDescr <- mdrrDescr[, -nzv]
diff --git a/pkg/caret/man/sensitivity.Rd b/pkg/caret/man/sensitivity.Rd
index 9a062e2f..8c9a8265 100644
--- a/pkg/caret/man/sensitivity.Rd
+++ b/pkg/caret/man/sensitivity.Rd
@@ -159,7 +159,7 @@ negPredValue(pred, truth, prevalence = 0.25)
 
 prev <- seq(0.001, .99, length = 20)
 npvVals <- ppvVals <- prev  * NA
-for(i in seq(along = prev))
+for(i in seq(along.with = prev))
   {
     ppvVals[i] <- posPredValue(pred, truth, prevalence = prev[i])
     npvVals[i] <- negPredValue(pred, truth, prevalence = prev[i])
diff --git a/pkg/caret/tests/testthat/test_resamples.R b/pkg/caret/tests/testthat/test_resamples.R
index d97d516e..1d3629ec 100644
--- a/pkg/caret/tests/testthat/test_resamples.R
+++ b/pkg/caret/tests/testthat/test_resamples.R
@@ -41,7 +41,7 @@ test_that('resample calculations', {
 
 test_that('test group-k-fold', {
   get_data <- function(n = 500) {
-    prevalence <- seq(.1, .9, length = 26)
+    prevalence <- seq(.1, .9, length.out = 26)
     dat <- sample(letters, size = n, replace = TRUE, prob = sample(prevalence))
     data.frame(grp = dat, stringsAsFactors = TRUE)
   }