diff --git a/DESCRIPTION b/DESCRIPTION
index c0ceacd2d..f74ff58f8 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: parsnip
 Title: A Common API to Modeling and Analysis Functions
-Version: 1.0.4.9003
+Version: 1.0.4.9004
 Authors@R: c(
     person("Max", "Kuhn", , "max@posit.co", role = c("aut", "cre")),
     person("Davis", "Vaughan", , "davis@posit.co", role = "aut"),
diff --git a/NAMESPACE b/NAMESPACE
index 21e02a22e..39e4b3bbb 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,5 +1,8 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(.censoring_weights_graf,default)
+S3method(.censoring_weights_graf,model_fit)
+S3method(.censoring_weights_graf,workflow)
 S3method(augment,model_fit)
 S3method(autoplot,glmnet)
 S3method(autoplot,model_fit)
@@ -144,6 +147,7 @@ S3method(varying_args,model_spec)
 S3method(varying_args,recipe)
 S3method(varying_args,step)
 export("%>%")
+export(.censoring_weights_graf)
 export(.check_glmnet_penalty_fit)
 export(.check_glmnet_penalty_predict)
 export(.cols)
diff --git a/R/ipcw.R b/R/ipcw.R
index ae1ef7bce..a21c5d6bf 100644
--- a/R/ipcw.R
+++ b/R/ipcw.R
@@ -19,6 +19,9 @@ trunc_probs <- function(probs, trunc = 0.01) {
 }
 
 .filter_eval_time <- function(eval_time, fail = TRUE) {
+  if (!is.null(eval_time)) {
+    eval_time <- as.numeric(eval_time)
+  }
   # will still propagate nulls:
   eval_time <- eval_time[!is.na(eval_time)]
   eval_time <- unique(eval_time)
@@ -32,3 +35,199 @@ trunc_probs <- function(probs, trunc = 0.01) {
   }
   eval_time
 }
+
+add_dot_row_to_weights <- function(dat, rows = NULL) {
+  if (is.null(rows)) {
+    dat <- add_rowindex(dat)
+  } else {
+    m <- length(rows)
+    n <- nrow(dat)
+    if (m != n) {
+      rlang::abort(
+        glue::glue(
+          "The length of 'rows' ({m}) should be equal to the number of rows in 'data' ({n})"
+        )
+      )
+    }
+    dat$.row <- rows
+  }
+  dat
+}
+
+.check_censor_model <- function(x) {
+  nms <- names(x)
+  if (!any(nms == "censor_probs")) {
+    rlang::abort("Please refit the model with parsnip version 1.0.4 or greater.")
+  }
+  invisible(NULL)
+}
+
+# nocov start
+# these are tested in extratests
+# ------------------------------------------------------------------------------
+# Brier score helpers. Most of this is based off of Graf, E., Schmoor, C.,
+# Sauerbrei, W. and Schumacher, M. (1999), Assessment and comparison of
+# prognostic classification schemes for survival data. _Statist. Med._, 18:
+# 2529-2545.
+
+# We need to use the time of analysis to determine what time to use to evaluate
+# the IPCWs.
+
+graf_weight_time <- function(surv_obj, eval_time, rows = NULL, eps = 10^-10) {
+  event_time <- .extract_surv_time(surv_obj)
+  status <- .extract_surv_status(surv_obj)
+  is_event_before_t <- event_time <= eval_time & status == 1
+  is_censored <- event_time > eval_time
+
+  # Three possible contributions to the statistic from Graf 1999
+
+  # Censoring time before eval_time, no contribution (Graf category 3)
+  weight_time <- rep(NA_real_, length(event_time))
+
+  # A real event prior to eval_time (Graf category 1)
+  weight_time[is_event_before_t] <- event_time[is_event_before_t] - eps
+
+  # Observed time greater than eval_time (Graf category 2)
+  weight_time[is_censored] <- eval_time - eps
+
+  weight_time <- ifelse(weight_time < 0, 0, weight_time)
+
+  res <- tibble::tibble(surv = surv_obj, weight_time = weight_time, eval_time)
+  add_dot_row_to_weights(res, rows)
+}
+
+# ------------------------------------------------------------------------------
+#' Calculations for inverse probability of censoring weights (IPCW)
+#'
+#' The method of Graf _et al_ (1999) is used to compute weights at specific
+#' evaluation times that can be used to help measure a model's time-dependent
+#' performance (e.g. the time-dependent Brier score or the area under the ROC
+#' curve).
+#' @param data A data frame with a column containing a [survival::Surv()] object.
+#' @param predictors Not currently used. A potential future slot for models with
+#' informative censoring based on columns in `data`.
+#' @param rows An optional integer vector with length equal to the number of
+#' rows in `data` that is used to index the original data. The default is to
+#' use a fresh index on data (i.e. `1:nrow(data)`).
+#' @param eval_time A vector of finite, non-negative times at which to
+#' compute the probability of censoring and the corresponding weights.
+#' @param object A fitted parsnip model object or fitted workflow with a mode
+#' of "censored regression".
+#' @param trunc A potential lower bound for the probability of censoring to avoid
+#' very large weight values.
+#' @param eps A small value that is subtracted from the evaluation time when
+#' computing the censoring probabilities. See Details below.
+#' @return A tibble with columns `.row`, `eval_time`, `.prob_cens` (the
+#' probability of being censored just prior to the evaluation time), and
+#' `.weight_cens` (the inverse probability of censoring weight).
+#' @details
+#'
+#' A probability that the data are censored immediately prior to a specific
+#' time is computed. To do this, we must determine what time to
+#' make the prediction. There are two time values for each row of the data set:
+#' the observed time (either censored or not) and the time that the model is
+#' being evaluated at (e.g. the survival function prediction at some time point),
+#' which is constant across rows. .
+#'
+#' From  Graf _et al_ (1999) there are three cases:
+#'
+#'  - If the observed time is a censoring time and that is before the
+#'    evaluation time, the data point should make no contribution to the
+#'    performance metric (their "category 3"). These values have a missing
+#'    value for their probability estimate (and also for their weight column).
+#'
+#'  - If the observed time corresponds to an actual event, and that time is
+#'    prior to the evaluation time (category 1), the probability of being
+#'    censored is predicted at the observed time (minus an epsilon).
+#'
+#'  - If the observed time is _after_ the evaluation time (category 2), regardless of
+#'    the status, the probability of being censored is predicted at the evaluation
+#'     time (minus an epsilon).
+#'
+#' The epsilon is used since, we would not have actual information at time `t`
+#' for a data point being predicted at time `t` (only data prior to time `t`
+#' should be available).
+#'
+#' After the censoring probability is computed, the `trunc` option is used to
+#' avoid using numbers pathologically close to zero. After this, the weight is
+#' computed by inverting the censoring probability.
+#'
+#' The `eps` argument is used to avoid information leakage when computing the
+#' censoring probability. Subtracting a small number avoids using data that
+#' would not be known at the time of prediction. For example, if we are making
+#' survival probability predictions at `eval_time = 3.0`, we would not know the
+#' about the probability of being censored at that exact time (since it has not
+#' occurred yet).
+#'
+#' Note that if there are `n` rows in `data` and `t` time points, the resulting
+#' data has `n * t` rows. Computations will not easily scale well as `t` becomes
+#' large.
+#' @references Graf, E., Schmoor, C., Sauerbrei, W. and Schumacher, M. (1999),
+#' Assessment and comparison of prognostic classification schemes for survival
+#' data. _Statist. Med._, 18: 2529-2545.
+#' @export
+#' @name censoring_weights
+#' @keywords internal
+.censoring_weights_graf <- function(object, ...) {
+  UseMethod(".censoring_weights_graf")
+}
+
+#' @export
+#' @rdname censoring_weights
+.censoring_weights_graf.default <- function(object, ...) {
+  cls <- paste0("'", class(object), "'", collapse = ", ")
+  msg <- paste("There is no `.censoring_weights_graf()` method for objects with class(es):",
+               cls)
+  rlang::abort(msg)
+}
+
+
+#' @export
+#' @rdname censoring_weights
+.censoring_weights_graf.workflow <- function(object,
+                                             data,
+                                             eval_time,
+                                             rows = NULL,
+                                             predictors = NULL,
+                                             trunc = 0.05, eps = 10^-10, ...) {
+  if (is.null(object$fit$fit)) {
+    rlang::abort("The workflow does not have a model fit object.", call = FALSE)
+  }
+  .censoring_weights_graf(object$fit$fit, data, eval_time, rows, predictors, trunc, eps)
+}
+
+#' @export
+#' @rdname censoring_weights
+.censoring_weights_graf.model_fit <- function(object,
+                                              data,
+                                              eval_time,
+                                              rows = NULL,
+                                              predictors = NULL,
+                                              trunc = 0.05, eps = 10^-10, ...) {
+  rlang::check_dots_empty()
+  .check_censor_model(object)
+  if (!is.null(predictors)) {
+    rlang::warn("The 'predictors' argument to the survival weighting function is not currently used.", call = FALSE)
+  }
+  eval_time <- .filter_eval_time(eval_time)
+
+  truth <- object$preproc$y_var
+  if (length(truth) != 1) {
+    # check_outcome() tests that the outcome column is a Surv object
+    rlang::abort("The event time data should be in a single column with class 'Surv'", call = FALSE)
+  }
+  surv_data <- dplyr::select(data, dplyr::all_of(!!truth)) %>% setNames("surv")
+  .check_censored_right(surv_data$surv)
+
+  purrr::map(eval_time,
+                 ~ graf_weight_time(surv_data$surv, .x, eps = eps, rows = rows))  %>%
+                 purrr::list_rbind() %>%
+    dplyr::mutate(
+      .prob_cens = predict(object$censor_probs, time = weight_time, as_vector = TRUE),
+      .prob_cens = trunc_probs(.prob_cens, trunc),
+      .weight_cens = 1 / .prob_cens
+    ) %>%
+    dplyr::select(.row, eval_time, .prob_cens, .weight_cens)
+}
+
+# nocov end
diff --git a/R/parsnip-package.R b/R/parsnip-package.R
index e2033ee3b..b63fb1f35 100644
--- a/R/parsnip-package.R
+++ b/R/parsnip-package.R
@@ -44,7 +44,7 @@ utils::globalVariables(
     "compute_intercept", "remove_intercept", "estimate", "term",
     "call_info", "component", "component_id", "func", "tunable", "label",
     "pkg", ".order", "item", "tunable", "has_ext", "id", "weights", "has_wts",
-    "protect", "s"
+    "protect", "weight_time", ".prob_cens", ".weight_cens", "s"
   )
 )
 
diff --git a/man/censoring_weights.Rd b/man/censoring_weights.Rd
new file mode 100644
index 000000000..cb9008f0c
--- /dev/null
+++ b/man/censoring_weights.Rd
@@ -0,0 +1,116 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ipcw.R
+\name{censoring_weights}
+\alias{censoring_weights}
+\alias{.censoring_weights_graf}
+\alias{.censoring_weights_graf.default}
+\alias{.censoring_weights_graf.workflow}
+\alias{.censoring_weights_graf.model_fit}
+\title{Calculations for inverse probability of censoring weights (IPCW)}
+\usage{
+.censoring_weights_graf(object, ...)
+
+\method{.censoring_weights_graf}{default}(object, ...)
+
+\method{.censoring_weights_graf}{workflow}(
+  object,
+  data,
+  eval_time,
+  rows = NULL,
+  predictors = NULL,
+  trunc = 0.05,
+  eps = 10^-10,
+  ...
+)
+
+\method{.censoring_weights_graf}{model_fit}(
+  object,
+  data,
+  eval_time,
+  rows = NULL,
+  predictors = NULL,
+  trunc = 0.05,
+  eps = 10^-10,
+  ...
+)
+}
+\arguments{
+\item{object}{A fitted parsnip model object or fitted workflow with a mode
+of "censored regression".}
+
+\item{data}{A data frame with a column containing a \code{\link[survival:Surv]{survival::Surv()}} object.}
+
+\item{eval_time}{A vector of finite, non-negative times at which to
+compute the probability of censoring and the corresponding weights.}
+
+\item{rows}{An optional integer vector with length equal to the number of
+rows in \code{data} that is used to index the original data. The default is to
+use a fresh index on data (i.e. \code{1:nrow(data)}).}
+
+\item{predictors}{Not currently used. A potential future slot for models with
+informative censoring based on columns in \code{data}.}
+
+\item{trunc}{A potential lower bound for the probability of censoring to avoid
+very large weight values.}
+
+\item{eps}{A small value that is subtracted from the evaluation time when
+computing the censoring probabilities. See Details below.}
+}
+\value{
+A tibble with columns \code{.row}, \code{eval_time}, \code{.prob_cens} (the
+probability of being censored just prior to the evaluation time), and
+\code{.weight_cens} (the inverse probability of censoring weight).
+}
+\description{
+The method of Graf \emph{et al} (1999) is used to compute weights at specific
+evaluation times that can be used to help measure a model's time-dependent
+performance (e.g. the time-dependent Brier score or the area under the ROC
+curve).
+}
+\details{
+A probability that the data are censored immediately prior to a specific
+time is computed. To do this, we must determine what time to
+make the prediction. There are two time values for each row of the data set:
+the observed time (either censored or not) and the time that the model is
+being evaluated at (e.g. the survival function prediction at some time point),
+which is constant across rows. .
+
+From  Graf \emph{et al} (1999) there are three cases:
+\itemize{
+\item If the observed time is a censoring time and that is before the
+evaluation time, the data point should make no contribution to the
+performance metric (their "category 3"). These values have a missing
+value for their probability estimate (and also for their weight column).
+\item If the observed time corresponds to an actual event, and that time is
+prior to the evaluation time (category 1), the probability of being
+censored is predicted at the observed time (minus an epsilon).
+\item If the observed time is \emph{after} the evaluation time (category 2), regardless of
+the status, the probability of being censored is predicted at the evaluation
+time (minus an epsilon).
+}
+
+The epsilon is used since, we would not have actual information at time \code{t}
+for a data point being predicted at time \code{t} (only data prior to time \code{t}
+should be available).
+
+After the censoring probability is computed, the \code{trunc} option is used to
+avoid using numbers pathologically close to zero. After this, the weight is
+computed by inverting the censoring probability.
+
+The \code{eps} argument is used to avoid information leakage when computing the
+censoring probability. Subtracting a small number avoids using data that
+would not be known at the time of prediction. For example, if we are making
+survival probability predictions at \code{eval_time = 3.0}, we would not know the
+about the probability of being censored at that exact time (since it has not
+occurred yet).
+
+Note that if there are \code{n} rows in \code{data} and \code{t} time points, the resulting
+data has \code{n * t} rows. Computations will not easily scale well as \code{t} becomes
+large.
+}
+\references{
+Graf, E., Schmoor, C., Sauerbrei, W. and Schumacher, M. (1999),
+Assessment and comparison of prognostic classification schemes for survival
+data. \emph{Statist. Med.}, 18: 2529-2545.
+}
+\keyword{internal}
diff --git a/tests/testthat/helper-objects.R b/tests/testthat/helper-objects.R
index 70eb0fb2e..a9297a65a 100644
--- a/tests/testthat/helper-objects.R
+++ b/tests/testthat/helper-objects.R
@@ -11,3 +11,16 @@ caught_ctrl   <- control_parsnip(verbosity = 1, catch = TRUE)
 quiet_ctrl    <- control_parsnip(verbosity = 0, catch = TRUE)
 
 run_glmnet <- utils::compareVersion('3.6.0', as.character(getRversion())) > 0
+
+# ------------------------------------------------------------------------------
+# for skips
+
+is_tf_ok <- function() {
+  tf_ver <- try(tensorflow::tf_version(), silent = TRUE)
+  if (inherits(tf_ver, "try-error")) {
+    res <- FALSE
+  } else {
+    res <- !is.null(tf_ver)
+  }
+  res
+}
diff --git a/tests/testthat/test-ipcw.R b/tests/testthat/test-ipcw.R
index 9a1b3ed3f..376d20816 100644
--- a/tests/testthat/test-ipcw.R
+++ b/tests/testthat/test-ipcw.R
@@ -17,8 +17,20 @@ test_that('probability truncation', {
     min(parsnip:::trunc_probs((1:200)/200)),
     1 / 200
   )
-})
 
+  probs_1 <- (0:10) / 20
+  probs_2 <- probs_1
+  probs_2[3] <- NA_real_
+
+  expect_equal(parsnip:::trunc_probs(probs_1, 0), probs_1)
+  expect_equal(parsnip:::trunc_probs(probs_2, 0), probs_2)
+  expect_equal(
+    parsnip:::trunc_probs(probs_1, 0.1),
+    ifelse(probs_1 < 0.05 / 2, 0.05 / 2, probs_1)
+  )
+  expect_equal(min(parsnip:::trunc_probs(probs_2, 0.1), na.rm = TRUE), 0.05 / 2)
+  expect_equal(is.na(parsnip:::trunc_probs(probs_2, 0.1)),is.na(probs_2))
+})
 
 test_that('time filtering', {
   times_1 <- 0:10
@@ -35,8 +47,3 @@ test_that('time filtering', {
   expect_snapshot(error = TRUE, parsnip:::.filter_eval_time(-1))
   expect_null(parsnip:::.filter_eval_time(NULL))
 })
-
-
-
-
-
diff --git a/tests/testthat/test_linear_reg_keras.R b/tests/testthat/test_linear_reg_keras.R
index f2d0b1dd8..fbf37f6c2 100644
--- a/tests/testthat/test_linear_reg_keras.R
+++ b/tests/testthat/test_linear_reg_keras.R
@@ -17,7 +17,7 @@ ctrl <- control_parsnip(verbosity = 0, catch = FALSE)
 test_that('model fitting', {
   skip_on_cran()
   skip_if_not_installed("keras")
-  skip_if(is.null(tensorflow::tf_version()))
+  skip_if(!is_tf_ok())
 
   set_tf_seed(257)
 
@@ -87,7 +87,7 @@ test_that('model fitting', {
 test_that('regression prediction', {
   skip_on_cran()
   skip_if_not_installed("keras")
-  skip_if(is.null(tensorflow::tf_version()))
+  skip_if(!is_tf_ok())
 
   library(keras)
 
diff --git a/tests/testthat/test_logistic_reg_keras.R b/tests/testthat/test_logistic_reg_keras.R
index 6e5897f58..2fae93320 100644
--- a/tests/testthat/test_logistic_reg_keras.R
+++ b/tests/testthat/test_logistic_reg_keras.R
@@ -27,7 +27,7 @@ ctrl <- control_parsnip(verbosity = 0, catch = FALSE)
 test_that('model fitting', {
   skip_on_cran()
   skip_if_not_installed("keras")
-  skip_if(is.null(tensorflow::tf_version()))
+  skip_if(!is_tf_ok())
 
   set_tf_seed(257)
 
@@ -97,7 +97,7 @@ test_that('model fitting', {
 test_that('classification prediction', {
   skip_on_cran()
   skip_if_not_installed("keras")
-  skip_if(is.null(tensorflow::tf_version()))
+  skip_if(!is_tf_ok())
 
   library(keras)
 
@@ -142,7 +142,7 @@ test_that('classification prediction', {
 test_that('classification probabilities', {
   skip_on_cran()
   skip_if_not_installed("keras")
-  skip_if(is.null(tensorflow::tf_version()))
+  skip_if(!is_tf_ok())
 
   library(keras)
 
diff --git a/tests/testthat/test_mlp_keras.R b/tests/testthat/test_mlp_keras.R
index 57ae81e94..939ca9f0c 100644
--- a/tests/testthat/test_mlp_keras.R
+++ b/tests/testthat/test_mlp_keras.R
@@ -14,7 +14,7 @@ nn_dat <- read.csv("nnet_test.txt")
 test_that('keras execution, classification', {
   skip_on_cran()
   skip_if_not_installed("keras")
-  skip_if(is.null(tensorflow::tf_version()))
+  skip_if(!is_tf_ok())
 
   expect_error(
     res <- parsnip::fit(
@@ -58,7 +58,7 @@ test_that('keras execution, classification', {
 test_that('keras classification prediction', {
   skip_on_cran()
   skip_if_not_installed("keras")
-  skip_if(is.null(tensorflow::tf_version()))
+  skip_if(!is_tf_ok())
   library(keras)
 
   xy_fit <- parsnip::fit_xy(
@@ -107,7 +107,7 @@ test_that('keras classification prediction', {
 test_that('keras classification probabilities', {
   skip_on_cran()
   skip_if_not_installed("keras")
-  skip_if(is.null(tensorflow::tf_version()))
+  skip_if(!is_tf_ok())
 
   xy_fit <- parsnip::fit_xy(
     hpc_keras,
@@ -158,7 +158,7 @@ bad_keras_reg <-
 test_that('keras execution, regression', {
   skip_on_cran()
   skip_if_not_installed("keras")
-  skip_if(is.null(tensorflow::tf_version()))
+  skip_if(!is_tf_ok())
 
   expect_error(
     res <- parsnip::fit(
@@ -186,7 +186,7 @@ test_that('keras execution, regression', {
 test_that('keras regression prediction', {
   skip_on_cran()
   skip_if_not_installed("keras")
-  skip_if(is.null(tensorflow::tf_version()))
+  skip_if(!is_tf_ok())
 
   xy_fit <- parsnip::fit_xy(
     mlp(mode = "regression", hidden_units = 2, epochs = 500, penalty = .1) %>%
@@ -220,7 +220,7 @@ test_that('keras regression prediction', {
 test_that('multivariate nnet formula', {
   skip_on_cran()
   skip_if_not_installed("keras")
-  skip_if(is.null(tensorflow::tf_version()))
+  skip_if(!is_tf_ok())
 
   nnet_form <-
     mlp(mode = "regression", hidden_units = 3, penalty = 0.01)  %>%
diff --git a/tests/testthat/test_multinom_reg_keras.R b/tests/testthat/test_multinom_reg_keras.R
index e321e9389..698ff2caf 100644
--- a/tests/testthat/test_multinom_reg_keras.R
+++ b/tests/testthat/test_multinom_reg_keras.R
@@ -25,7 +25,7 @@ ctrl <- control_parsnip(verbosity = 0, catch = FALSE)
 test_that('model fitting', {
   skip_on_cran()
   skip_if_not_installed("keras")
-  skip_if(is.null(tensorflow::tf_version()))
+  skip_if(!is_tf_ok())
 
   set_tf_seed(257)
 
@@ -95,7 +95,7 @@ test_that('model fitting', {
 test_that('classification prediction', {
   skip_on_cran()
   skip_if_not_installed("keras")
-  skip_if(is.null(tensorflow::tf_version()))
+  skip_if(!is_tf_ok())
 
   library(keras)
 
@@ -140,7 +140,7 @@ test_that('classification prediction', {
 test_that('classification probabilities', {
   skip_on_cran()
   skip_if_not_installed("keras")
-  skip_if(is.null(tensorflow::tf_version()))
+  skip_if(!is_tf_ok())
 
   library(keras)