diff --git a/NEWS.md b/NEWS.md
index 195780ae9..990cdc821 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -14,9 +14,11 @@
 
 ## Other Changes
 
- * When the xy interface is used and the underlying model expects to use a matrix, a better warning is issued when predictors contain non-numeric columns (including dates). 
+* When the xy interface is used and the underlying model expects to use a matrix, a better warning is issued when predictors contain non-numeric columns (including dates). 
  
- * The fit time is only calculated when the `verbosity` argument of `control_parsnip()` is 2L or greater. Also, the call to `system.time()` now uses `gcFirst = FALSE`. (#611)
+* The fit time is only calculated when the `verbosity` argument of `control_parsnip()` is 2L or greater. Also, the call to `system.time()` now uses `gcFirst = FALSE`. (#611)
+ 
+* Argument `interval` was added for prediction: For types "survival" and "quantile", estimates for the confidence or prediction interval can be added if available (#615).
 
 # parsnip 0.1.7
 
diff --git a/R/bart.R b/R/bart.R
index d527d67b2..06377be09 100644
--- a/R/bart.R
+++ b/R/bart.R
@@ -162,10 +162,11 @@ update.bart <-
   }
 
 
+#' Developer functions for predictions via BART models
 #' @export
 #' @keywords internal
 #' @name bart-internal
-#' @inherit predict.model_fit
+#' @inheritParams predict.model_fit
 #' @param obj A parsnip object.
 #' @param ci Confidence (TRUE) or prediction interval (FALSE)
 #' @param level Confidence level.
diff --git a/R/predict.R b/R/predict.R
index 18611acee..abb366499 100644
--- a/R/predict.R
+++ b/R/predict.R
@@ -19,8 +19,11 @@
 #'  `parsnip` related options that can be passed, depending on the
 #'  value of `type`. Possible arguments are:
 #'  \itemize{
-#'     \item `level`: for `type`s of "conf_int" and "pred_int" this
-#'            is the parameter for the tail area of the intervals
+#'     \item `interval`: for `type`s of "survival" and "quantile", should
+#'            interval estimates be added, if available? Options are `"none"`
+#'            and `"confidence"`.
+#'     \item `level`: for `type`s of "conf_int", "pred_int", and "survival"
+#'            this is the parameter for the tail area of the intervals
 #'            (e.g. confidence level for confidence intervals).
 #'            Default value is 0.95.
 #'     \item `std_error`: add the standard error of fit or prediction (on
@@ -82,12 +85,10 @@
 #' For censored regression:
 #'
 #'  * `type = "time"` produces a column `.pred_time`.
-#'  * `type = "hazard"` results in a column `.pred_hazard`.
-#'  * `type = "survival"` results in a list column containing tibbles with a
-#'     `.pred_survival` column.
-#'
-#'  For the last two types, the results are a nested tibble with an overall
-#'  column called `.pred` with sub-tibbles with the above format.
+#'  * `type = "hazard"` results in a list column `.pred` containing tibbles
+#'     with a column `.pred_hazard`.
+#'  * `type = "survival"` results in a list column `.pred` containing tibbles
+#'     with a `.pred_survival` column.
 #'
 #' In the case of Spark-based models, since table columns cannot
 #'  contain dots, the same convention is used except 1) no dots
@@ -98,6 +99,7 @@
 #'  `predict()` function will return the same structure as above but
 #'  filled with missing values. This does not currently work for
 #'  multivariate models.
+#'
 #' @examples
 #' library(dplyr)
 #'
@@ -309,7 +311,7 @@ check_pred_type_dots <- function(object, type, ...) {
 
   # ----------------------------------------------------------------------------
 
-  other_args <- c("level", "std_error", "quantile", "time", "increasing")
+  other_args <- c("interval", "level", "std_error", "quantile", "time", "increasing")
   is_pred_arg <- names(the_dots) %in% other_args
   if (any(!is_pred_arg)) {
     bad_args <- names(the_dots)[!is_pred_arg]
diff --git a/R/predict_quantile.R b/R/predict_quantile.R
index 7965069e6..c2817e48b 100644
--- a/R/predict_quantile.R
+++ b/R/predict_quantile.R
@@ -1,13 +1,17 @@
 #' @keywords internal
 #' @rdname other_predict
-#' @param quant A vector of numbers between 0 and 1 for the quantile being
+#' @param quantile A vector of numbers between 0 and 1 for the quantile being
 #'  predicted.
 #' @inheritParams predict.model_fit
 #' @method predict_quantile model_fit
 #' @export predict_quantile.model_fit
 #' @export
-predict_quantile.model_fit <-
-  function(object, new_data, quantile = (1:9)/10, ...) {
+predict_quantile.model_fit <- function(object,
+                                       new_data,
+                                       quantile = (1:9)/10,
+                                       interval = "none",
+                                       level = 0.95,
+                                       ...) {
 
     check_spec_pred_type(object, "quantile")
 
diff --git a/R/predict_survival.R b/R/predict_survival.R
index eb87a08ad..fe58c62cb 100644
--- a/R/predict_survival.R
+++ b/R/predict_survival.R
@@ -5,7 +5,7 @@
 #' @export predict_survival.model_fit
 #' @export
 predict_survival.model_fit <-
-  function(object, new_data, time, ...) {
+  function(object, new_data, time, interval = "none", level = 0.95, ...) {
 
     check_spec_pred_type(object, "survival")
 
diff --git a/man/bart-internal.Rd b/man/bart-internal.Rd
index 577d05dda..690f715f7 100644
--- a/man/bart-internal.Rd
+++ b/man/bart-internal.Rd
@@ -4,7 +4,7 @@
 \alias{bart-internal}
 \alias{bartMachine_interval_calc}
 \alias{dbart_predict_calc}
-\title{Model predictions}
+\title{Developer functions for predictions via BART models}
 \usage{
 bartMachine_interval_calc(new_data, obj, ci = TRUE, level = 0.95)
 
@@ -26,112 +26,7 @@ appropriate value based on the model's mode.}
 
 \item{std_err}{Attach column for standard error of prediction or not.}
 }
-\value{
-With the exception of \code{type = "raw"}, the results of
-\code{predict.model_fit()} will be a tibble as many rows in the output
-as there are rows in \code{new_data} and the column names will be
-predictable.
-
-For numeric results with a single outcome, the tibble will have
-a \code{.pred} column and \code{.pred_Yname} for multivariate results.
-
-For hard class predictions, the column is named \code{.pred_class}
-and, when \code{type = "prob"}, the columns are \code{.pred_classlevel}.
-
-\code{type = "conf_int"} and \code{type = "pred_int"} return tibbles with
-columns \code{.pred_lower} and \code{.pred_upper} with an attribute for
-the confidence level. In the case where intervals can be
-produces for class probabilities (or other non-scalar outputs),
-the columns will be named \code{.pred_lower_classlevel} and so on.
-
-Quantile predictions return a tibble with a column \code{.pred}, which is
-a list-column. Each list element contains a tibble with columns
-\code{.pred} and \code{.quantile} (and perhaps other columns).
-
-Using \code{type = "raw"} with \code{predict.model_fit()} will return
-the unadulterated results of the prediction function.
-
-For censored regression:
-\itemize{
-\item \code{type = "time"} produces a column \code{.pred_time}.
-\item \code{type = "hazard"} results in a column \code{.pred_hazard}.
-\item \code{type = "survival"} results in a list column containing tibbles with a
-\code{.pred_survival} column.
-}
-
-For the last two types, the results are a nested tibble with an overall
-column called \code{.pred} with sub-tibbles with the above format.
-
-In the case of Spark-based models, since table columns cannot
-contain dots, the same convention is used except 1) no dots
-appear in names and 2) vectors are never returned but
-type-specific prediction functions.
-
-When the model fit failed and the error was captured, the
-\code{predict()} function will return the same structure as above but
-filled with missing values. This does not currently work for
-multivariate models.
-}
 \description{
-Apply a model to create different types of predictions.
-\code{predict()} can be used for all types of models and uses the
-"type" argument for more specificity.
-}
-\details{
-If "type" is not supplied to \code{predict()}, then a choice
-is made:
-\itemize{
-\item \code{type = "numeric"} for regression models,
-\item \code{type = "class"} for classification, and
-\item \code{type = "time"} for censored regression.
-}
-
-\code{predict()} is designed to provide a tidy result (see "Value"
-section below) in a tibble output format.
-\subsection{Interval predictions}{
-
-When using \code{type = "conf_int"} and \code{type = "pred_int"}, the options
-\code{level} and \code{std_error} can be used. The latter is a logical for an
-extra column of standard error values (if available).
-}
-
-\subsection{Censored regression predictions}{
-
-For censored regression, a numeric vector for \code{time} is required when
-survival or hazard probabilities are requested. Also, when
-\code{type = "linear_pred"}, censored regression models will by default be
-formatted such that the linear predictor \emph{increases} with time. This may
-have the opposite sign as what the underlying model's \code{predict()} method
-produces. Set \code{increasing = FALSE} to suppress this behavior.
-}
-}
-\examples{
-library(dplyr)
-
-lm_model <-
-  linear_reg() \%>\%
-  set_engine("lm") \%>\%
-  fit(mpg ~ ., data = mtcars \%>\% dplyr::slice(11:32))
-
-pred_cars <-
-  mtcars \%>\%
-  dplyr::slice(1:10) \%>\%
-  dplyr::select(-mpg)
-
-predict(lm_model, pred_cars)
-
-predict(
-  lm_model,
-  pred_cars,
-  type = "conf_int",
-  level = 0.90
-)
-
-predict(
-  lm_model,
-  pred_cars,
-  type = "raw",
-  opts = list(type = "terms")
-)
+Developer functions for predictions via BART models
 }
 \keyword{internal}
diff --git a/man/other_predict.Rd b/man/other_predict.Rd
index dd02edb8b..ee49f70d1 100644
--- a/man/other_predict.Rd
+++ b/man/other_predict.Rd
@@ -35,9 +35,16 @@ predict_linear_pred(object, ...)
 
 predict_numeric(object, ...)
 
-\method{predict_quantile}{model_fit}(object, new_data, quantile = (1:9)/10, ...)
+\method{predict_quantile}{model_fit}(
+  object,
+  new_data,
+  quantile = (1:9)/10,
+  interval = "none",
+  level = 0.95,
+  ...
+)
 
-\method{predict_survival}{model_fit}(object, new_data, time, ...)
+\method{predict_survival}{model_fit}(object, new_data, time, interval = "none", level = 0.95, ...)
 
 predict_survival(object, ...)
 
@@ -55,8 +62,11 @@ function cannot be passed here (see \code{opts}). There are some
 \code{parsnip} related options that can be passed, depending on the
 value of \code{type}. Possible arguments are:
 \itemize{
-\item \code{level}: for \code{type}s of "conf_int" and "pred_int" this
-is the parameter for the tail area of the intervals
+\item \code{interval}: for \code{type}s of "survival" and "quantile", should
+interval estimates be added, if available? Options are \code{"none"}
+and \code{"confidence"}.
+\item \code{level}: for \code{type}s of "conf_int", "pred_int", and "survival"
+this is the parameter for the tail area of the intervals
 (e.g. confidence level for confidence intervals).
 Default value is 0.95.
 \item \code{std_error}: add the standard error of fit or prediction (on
@@ -73,7 +83,7 @@ interval estimates.}
 \item{std_error}{A single logical for whether the standard error should be
 returned (assuming that the model can compute it).}
 
-\item{quant}{A vector of numbers between 0 and 1 for the quantile being
+\item{quantile}{A vector of numbers between 0 and 1 for the quantile being
 predicted.}
 }
 \description{
diff --git a/man/predict.model_fit.Rd b/man/predict.model_fit.Rd
index ae645fa5e..4632bb205 100644
--- a/man/predict.model_fit.Rd
+++ b/man/predict.model_fit.Rd
@@ -32,8 +32,11 @@ function cannot be passed here (see \code{opts}). There are some
 \code{parsnip} related options that can be passed, depending on the
 value of \code{type}. Possible arguments are:
 \itemize{
-\item \code{level}: for \code{type}s of "conf_int" and "pred_int" this
-is the parameter for the tail area of the intervals
+\item \code{interval}: for \code{type}s of "survival" and "quantile", should
+interval estimates be added, if available? Options are \code{"none"}
+and \code{"confidence"}.
+\item \code{level}: for \code{type}s of "conf_int", "pred_int", and "survival"
+this is the parameter for the tail area of the intervals
 (e.g. confidence level for confidence intervals).
 Default value is 0.95.
 \item \code{std_error}: add the standard error of fit or prediction (on
@@ -72,14 +75,12 @@ the unadulterated results of the prediction function.
 For censored regression:
 \itemize{
 \item \code{type = "time"} produces a column \code{.pred_time}.
-\item \code{type = "hazard"} results in a column \code{.pred_hazard}.
-\item \code{type = "survival"} results in a list column containing tibbles with a
-\code{.pred_survival} column.
+\item \code{type = "hazard"} results in a list column \code{.pred} containing tibbles
+with a column \code{.pred_hazard}.
+\item \code{type = "survival"} results in a list column \code{.pred} containing tibbles
+with a \code{.pred_survival} column.
 }
 
-For the last two types, the results are a nested tibble with an overall
-column called \code{.pred} with sub-tibbles with the above format.
-
 In the case of Spark-based models, since table columns cannot
 contain dots, the same convention is used except 1) no dots
 appear in names and 2) vectors are never returned but