diff --git a/skpro/regression/cyclic_boosting.py b/skpro/regression/cyclic_boosting.py
index 230f0c5f7..ad4bf836a 100644
--- a/skpro/regression/cyclic_boosting.py
+++ b/skpro/regression/cyclic_boosting.py
@@ -24,11 +24,24 @@
 
 
 class CyclicBoosting(BaseProbaRegressor):
-    """Cyclic boosting regressor.
+    """Cyclic boosting regressor from ``cyclic-boosting`` library.
 
-    Estimates the parameters of Johnson Quantile-Parameterized Distributions
-    (JQPD) by quantile regression, which is one of the Cyclic boosting's functions
-    this method can more accurately approximate to the distribution of observed data
+    Direct interface to ``pipeline_CBAdditiveQuantileRegressor``
+    and ``pipeline_CBMultiplicativeQuantileRegressor`` from ``cyclic-boosting``.
+
+    The algorithms use boosting to create conditional distribution predictions
+    that are Johnson Quantile-Parameterized Distributions (JQPD),
+    with parameters estimated by quantile regression at quantile nodes.
+
+    The quantile nodes are ``[alpha, 0.5, 1-alpha]``, where ``alpha``
+    is a parameter of the model.
+
+    The cyclic boosting model performs boosted quantile regression for the quantiles
+    at the nodes, and then substitutes the quantile predictions into the paramtric
+    form of the Johnson QPD.
+
+    The model allows to select unbounded, left semi-bounded, and bounded
+    predictive distribution support.
 
     Parameters
     ----------
@@ -46,23 +59,38 @@ class CyclicBoosting(BaseProbaRegressor):
         for basic options, see https://cyclic-boosting.readthedocs.io/en/latest/\
         tutorial.html#set-feature-properties
     alpha : float, default=0.2
-        lower quantile for QPD's parameter alpha
+        lower quantile QPD parameter.
+        The three quantile nodes are uniquely determined by this parameter,
+        as ``[alpha, 0.5, 1-alpha]``.
     mode : str, default='multiplicative'
         the type of quantile regressor. 'multiplicative' or 'additive'
+    bound : str, default='U', one of ``'S'``, ``'B'``, ``'U'``
+        Mode for the predictive distribution support, options are ``S``
+        (semi-bounded), ``B`` (bounded), and ``U`` (unbounded).
     lower : float, default=None
-        lower bound of supported range (only active for bound and semi-bound
-        modes). If neither 'lower' nor 'upper' is specified, `QPD_U` will be used as
-        unbound-mode
+        lower bound of predictive distribution support.
+        If ``None`` (default), ``upper`` should also be ``None``, and the
+        predictive distibution will have unbounded support, i.e., the entire reals.
+        If a float, and ``upper`` is ``None``, prediction will be of
+        semi-bounded support, with support between ``lower`` and infinity.
+        If a float, and ``upper`` is also a float, prediction will be on a bounded
+        interval, with support between ``lower`` and ``upper``.
     upper : float, default=None
-        upper bound of supported range (only active for bound mode). If neither
-        'lower' nor 'upper' is specified, `QPD_U` will be used as unbound-mode
-    version: str, one of ``'normal'`` (default), ``'logistic'``
-        options are ``'normal'`` (default) or ``'logistic'``
+        upper bound of predictive distribution support.
+        If ``None`` (default), will use semi-bounded mode if ``lower`` is a float,
+        and unbounded if ``lower`` is ``None``.
+        If a float, assumes that ``lower`` is also a float, and prediction will
+        be on a bounded interval, with support between ``lower`` and ``upper``.
+    maximal_iterations : int, default=10
+        maximum number of iterations for the cyclic boosting algorithm
+    dist_type: str, one of ``'normal'`` (default), ``'logistic'``
+        inner base distirbution to use for the Johnson QPD, i.e., before
+        arcosh and similar transformations.
+        Available options are ``'normal'`` (default), ``'logistic'``,
+        or ``'sinhlogistic'``.
     dist_shape: float, optional, default=0.0
         parameter modifying the logistic base distribution via
-        sinh/arcsinh-scaling (only active in sinhlogistic version)
-    maximal_iterations : int, default=10
-        number of iterations
+        sinh/arcsinh-scaling - only relevant for ``dist_type='sinhlogistic'``
 
     Attributes
     ----------
@@ -111,28 +139,61 @@ def __init__(
         feature_properties=None,
         alpha=0.2,
         mode="multiplicative",
+        bound="deprecated",
         lower=None,
         upper=None,
-        version: Union[str, None] = "normal",
-        dist_shape: Union[float, None] = 0.0,
         maximal_iterations=10,
+        dist_type: Union[str, None] = "normal",
+        dist_shape: Union[float, None] = 0.0,
     ):
         self.feature_groups = feature_groups
         self.feature_properties = feature_properties
         self.alpha = alpha
-        self.quantiles = [self.alpha, 0.5, 1 - self.alpha]
-        self.quantile_values = list()
-        self.quantile_est = list()
-        self.qpd = None
         self.mode = mode
+        self.bound = bound
         self.lower = lower
         self.upper = upper
-        self.version = version
-        self.dist_shape = dist_shape
         self.maximal_iterations = maximal_iterations
+        self.dist_type = dist_type
+        self.dist_shape = dist_shape
 
         super().__init__()
 
+        self.quantiles = [self.alpha, 0.5, 1 - self.alpha]
+        self.quantile_values = list()
+        self.quantile_est = list()
+        self.qpd = None
+
+        # todo 2.4.0: remove bound parameter and this deprecation warning
+        if bound == "deprecated":
+            warnings.warn(
+                "In CyclicBoosting, the 'bound' parameter is deprecated, "
+                "and will be removed in skpro version 2.4.0. "
+                "To retain the current behavior, and silence this warning, "
+                "do not set the 'bound' parameter "
+                "and set 'lower' and 'upper' parameters instead, "
+                "as follows: for unbounded mode, previously bound='U', "
+                "set 'lower' and 'upper' to None; "
+                "for semi-bounded mode, previously bound='S', "
+                "set 'lower' to lower bound and 'upper' to None; "
+                "for bounded mode, previously bound='B', "
+                "set 'lower' to lower bound and 'upper' to upper bound.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+
+        # todo 2.4.0: remove this block
+        # translate bound to lower and upper
+        if lower is None and bound in ["S", "B"]:
+            self._lower = 0.0
+        else:
+            self._lower = None
+        if upper is None and bound == "B":
+            self._upper = 1.0
+        else:
+            self._upper = upper
+        # end block
+
         # check parameters
         if (not isinstance(feature_groups, list)) and feature_groups is not None:
             raise ValueError("feature_groups needs to be list")
@@ -281,6 +342,7 @@ def _predict_proba(self, X):
             yhat = est.predict(X.copy())
             self.quantile_values.append(yhat)
 
+        # todo 2.4.0: replace self._lower and self._upper with self.lower and self.upper
         # Johnson Quantile-Parameterized Distributions
         params = {
             "alpha": self.alpha,
@@ -289,7 +351,7 @@ def _predict_proba(self, X):
             "qv_high": self.quantile_values[2].reshape(-1, 1),
             "lower": self.lower,
             "upper": self.upper,
-            "version": self.version,
+            "version": self.dist_type,
             "dist_shape": self.dist_shape,
             "index": index,
             "columns": y_cols,