[ENH] Cyclic boosting interface

.all-contributorsrc

@@ -99,6 +99,27 @@
         "doc",
         "ideas"
       ]
+    },
+    {
+      "login": "FelixWick",
+      "name": "Felix Wick",
+      "avatar_url": "https://avatars.githubusercontent.com/u/7291058?v=4",
+      "profile": "https://github.com/FelixWick",
+      "contributions": [
+        "ideas",
+        "review"
+      ]
+    },
+    {
+      "login": "setoguchi-naoki",
+      "name": "Naoki Setoguchi",
+      "avatar_url": "https://avatars.githubusercontent.com/u/104494531?v=4",
+      "profile": "https://github.com/setoguchi-naoki",
+      "contributions": [
+        "code",
+        "doc",
+        "test"
+      ]
     }
   ]
 }

docs/source/api_reference/distributions.rst

@@ -35,6 +35,9 @@ Parametric distributions
     Laplace
     Normal
     TDistribution
+    QPD_U
+    QPD_S
+    QPD_B
 
 Non-parametric and empirical distributions
 ------------------------------------------

docs/source/api_reference/regression.rst

@@ -79,6 +79,14 @@ take one or multiple ``sklearn`` estimators and adda probabilistic prediction mo
 
     MapieRegressor
 
+.. currentmodule:: skpro.regression.cyclic_boosting
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    CyclicBoosting
+
 Linear regression
 -----------------
 

pyproject.toml

@@ -61,6 +61,7 @@ all_extras = [
     "statsmodels>=0.12.1",
     "tabulate",
     "uncertainties",
+    "cyclic-boosting>=1.2.5; python_version < '3.12'"
 ]
 
 dev = [

skpro/distributions/__init__.py

@@ -8,10 +8,14 @@
     "Mixture",
     "Normal",
     "TDistribution",
+    "QPD_S",
+    "QPD_B",
+    "QPD_U",
 ]
 
 from skpro.distributions.empirical import Empirical
 from skpro.distributions.laplace import Laplace
 from skpro.distributions.mixture import Mixture
 from skpro.distributions.normal import Normal
+from skpro.distributions.qpd import QPD_B, QPD_S, QPD_U
 from skpro.distributions.t import TDistribution