nci · tennlee · Feb 12, 2025 · Jan 22, 2025 · Jan 23, 2025 · May 23, 2024
diff --git a/.zenodo.json b/.zenodo.json
@@ -74,6 +74,11 @@
             "orcid": "https://orcid.org/0009-0006-7361-163X",
             "affiliation": "Independent Contributor, Australia",
             "name": "Bluett, Liam"
+        },
+        {
+            "orcid": "https://orcid.org/0000-0003-3271-6874",
+            "affiliation": "Australian National University, Australia",
+            "name": "Squire, Dougal T."
         }
     ],    
     "license": "Apache-2.0",

diff --git a/docs/api.md b/docs/api.md
@@ -87,6 +87,7 @@
 
 ## scores.processing
 ```{eval-rst}
+.. autofunction:: scores.processing.block_bootstrap
 .. autofunction:: scores.processing.isotonic_fit
 .. autofunction:: scores.processing.broadcast_and_match_nan
 .. autofunction:: scores.processing.comparative_discretise

diff --git a/docs/included.md b/docs/included.md
@@ -649,6 +649,9 @@
 * - Binary Discretise Proportion
   - [API](api.md#scores.processing.binary_discretise_proportion)    
   - Flip-Flop Index
+* - Block Bootstrap
+  - [API](api.md#scores.processing.block_bootstrap)
+  - Confidence intervals. See [tutorial](project:./tutorials/Block_Bootstrapping.md)
 * - Broadcast and Match Not-a-Number (NaN)
   - [API](api.md#scores.processing.broadcast_and_match_nan)   
   - Murphy Score (Mean Elementary Score)

diff --git a/src/scores/__init__.py b/src/scores/__init__.py
@@ -6,6 +6,7 @@
 
 import scores.categorical
 import scores.continuous
+import scores.emerging
 import scores.functions
 import scores.pandas
 import scores.probability
@@ -19,6 +20,7 @@
     "scores.categorical",
     "scores.contingency",
     "scores.continuous",
+    "scores.emerging",
     "scores.functions",
     "scores.pandas",
     "scores.probability",

diff --git a/src/scores/processing/__init__.py b/src/scores/processing/__init__.py
@@ -2,6 +2,7 @@
 Import the functions from the implementations into the public API
 """
 
+from scores.processing.block_bootstrap_impl import block_bootstrap
 from scores.processing.discretise import (
     binary_discretise,
     binary_discretise_proportion,
@@ -18,4 +19,5 @@
     "broadcast_and_match_nan",
     "binary_discretise_proportion",
     "isotonic_fit",
+    "block_bootstrap",
 ]