diff --git a/DESCRIPTION b/DESCRIPTION
index 9ba94a2a..571d78d4 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,10 +1,12 @@
 Package: ranger
 Type: Package
 Title: A Fast Implementation of Random Forests
-Version: 0.16.4
-Date: 2024-10-28
-Author: Marvin N. Wright [aut, cre], Stefan Wager [ctb], Philipp Probst [ctb]
-Maintainer: Marvin N. Wright <cran@wrig.de>
+Version: 0.17.0
+Date: 2024-11-08
+Authors@R: c(
+    person("Marvin N.", "Wright", email = "cran@wrig.de", role = c("aut", "cre")),
+    person("Stefan", "Wager", role = "ctb"),
+    person("Philipp", "Probst", role = "ctb"))
 Description: A fast implementation of Random Forests, particularly suited for high
           dimensional data. Ensembles of classification, regression, survival and
           probability prediction trees are supported. Data from genome-wide association
diff --git a/NAMESPACE b/NAMESPACE
index 64d06c48..6dd59911 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -11,6 +11,8 @@ S3method(print,deforest.ranger)
 S3method(print,ranger)
 S3method(print,ranger.forest)
 S3method(print,ranger.prediction)
+S3method(str,ranger)
+S3method(str,ranger.forest)
 S3method(timepoints,ranger)
 S3method(timepoints,ranger.prediction)
 export(csrf)
diff --git a/NEWS b/NEWS
index ed9481ca..db4e8b6c 100644
--- a/NEWS
+++ b/NEWS
@@ -1,4 +1,12 @@
 
+##### Version 0.17.0
+* Set num.threads=2 as default; respect environment variables and options
+* Add handling of missing values for classification and regression
+* Allow vector min.node.size and min.bucket for class-specific limits
+* Add Poisson splitting rule for regression trees
+* Add hierarchical shrinkage
+* Fix a bug for always.split.variables (for some settings)
+
 ##### Version 0.16.0
 * Add node.stats option to save node statistics of all nodes
 * Add time.interest option to restrict unique survival times (faster and saves memory)
diff --git a/NEWS.md b/NEWS.md
index 32d0a57a..ffe77060 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,7 @@
 
+# ranger 0.17.0
+* New CRAN version
+
 # ranger 0.16.4
 * Add handling of missing values for classification and regression
 
diff --git a/R/hshrink.R b/R/hshrink.R
index 001866f4..45794e84 100644
--- a/R/hshrink.R
+++ b/R/hshrink.R
@@ -39,6 +39,9 @@
 #' @return The ranger object is modified in-place. 
 #'
 #' @examples
+#' ## Hierarchical shrinkage for a probablity forest
+#' rf <- ranger(Species ~ ., iris, node.stats = TRUE, probability = TRUE)
+#' hshrink(rf, lambda = 5)
 ##' @references
 ##' \itemize{
 ##'   \item Agarwal, A., Tan, Y.S., Ronen, O., Singh, C. & Yu, B. (2022). Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models. Proceedings of the 39th International Conference on Machine Learning, PMLR 162:111-135.
diff --git a/R/print.R b/R/print.R
index 90e78bc2..8f02bf09 100644
--- a/R/print.R
+++ b/R/print.R
@@ -103,11 +103,13 @@ print.ranger.prediction <- function(x, ...) {
   }
 }
 
+##' @export
 str.ranger.forest <- function(object, max.level = 2, ...) {
   class(object) <- "list"
   str(object, max.level = max.level, ...)
 }
 
+##' @export
 str.ranger <- function(object, max.level = 2, ...) {
   class(object) <- "list"
   str(object, max.level = max.level, ...)
diff --git a/cpp_version/src/version.h b/cpp_version/src/version.h
index 4aeaffdf..c417d41b 100644
--- a/cpp_version/src/version.h
+++ b/cpp_version/src/version.h
@@ -1,3 +1,3 @@
 #ifndef RANGER_VERSION
-#define RANGER_VERSION "0.16.3"
+#define RANGER_VERSION "0.17.0"
 #endif
diff --git a/man/hshrink.Rd b/man/hshrink.Rd
index e48c9a2e..fd813e59 100644
--- a/man/hshrink.Rd
+++ b/man/hshrink.Rd
@@ -19,6 +19,11 @@ Apply hierarchical shrinkage to a ranger object.
 Hierarchical shrinkage is a regularization technique that recursively shrinks node predictions towards parent node predictions. 
 For details see Agarwal et al. (2022).
 }
+\examples{
+## Hierarchical shrinkage for a probablity forest
+rf <- ranger(Species ~ ., iris, node.stats = TRUE, probability = TRUE)
+hshrink(rf, lambda = 5)
+}
 \references{
 \itemize{
   \item Agarwal, A., Tan, Y.S., Ronen, O., Singh, C. & Yu, B. (2022). Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models. Proceedings of the 39th International Conference on Machine Learning, PMLR 162:111-135.