Add 13 new procedural datasets across 7 categories

New dataset categories: combinatorics, statistics, optimization, and
formal languages. Extended existing algebra, arithmetic, probability,
logic, and graphs packages with complex_advanced, linear_algebra, limits,
number_theory, conditional_probability, set_operations, and job_scheduling.

Each dataset includes config validation, deterministic seeding, custom
scoring, curriculum support, and comprehensive unit tests (92 new tests).

tests/test_descriptive_stats.py

@@ -0,0 +1,78 @@
+import pytest
+
+from reasoning_gym.statistics.descriptive_stats import (
+    DescriptiveStatsConfig,
+    DescriptiveStatsCurriculum,
+    DescriptiveStatsDataset,
+)
+
+
+def test_config_validation():
+    with pytest.raises(AssertionError):
+        config = DescriptiveStatsConfig(min_data_size=2)
+        config.validate()
+
+    with pytest.raises(AssertionError):
+        config = DescriptiveStatsConfig(min_data_size=10, max_data_size=5)
+        config.validate()
+
+
+def test_deterministic():
+    config = DescriptiveStatsConfig(seed=42, size=10)
+    ds1 = DescriptiveStatsDataset(config)
+    ds2 = DescriptiveStatsDataset(config)
+    for i in range(len(ds1)):
+        assert ds1[i] == ds2[i]
+
+
+def test_item_structure():
+    config = DescriptiveStatsConfig(seed=42, size=50)
+    ds = DescriptiveStatsDataset(config)
+    for i in range(len(ds)):
+        item = ds[i]
+        assert isinstance(item, dict)
+        assert "question" in item
+        assert "answer" in item
+        assert "metadata" in item
+        assert item["metadata"]["source_dataset"] == "descriptive_stats"
+
+
+def test_answer_correctness():
+    config = DescriptiveStatsConfig(seed=42, size=50)
+    ds = DescriptiveStatsDataset(config)
+    for i in range(len(ds)):
+        item = ds[i]
+        score = ds.score_answer(item["answer"], item)
+        assert score >= 1.0, f"Item {i}: oracle answer scored {score}"
+
+
+def test_score_wrong_answer():
+    config = DescriptiveStatsConfig(seed=42, size=10)
+    ds = DescriptiveStatsDataset(config)
+    item = ds[0]
+    assert ds.score_answer(None, item) == 0.0
+    assert ds.score_answer("definitely wrong", item) == 0.0
+
+
+def test_curriculum():
+    curriculum = DescriptiveStatsCurriculum()
+    base_value = {"size": 50, "seed": 1}
+    base_cfg = curriculum.generate_configuration(base_value)
+    assert base_cfg.seed == 1
+
+    curriculum.increment_attr_level("data_size")
+    increased_cfg = curriculum.generate_configuration(base_value)
+    assert increased_cfg.max_data_size >= base_cfg.max_data_size
+
+
+def test_task_types():
+    for task_type in ("mean", "median", "mode", "weighted_mean", "std_dev", "percentile", "z_score"):
+        config = DescriptiveStatsConfig(
+            seed=42, size=10, task_types=(task_type,), task_weights=[1.0]
+        )
+        ds = DescriptiveStatsDataset(config)
+        for i in range(len(ds)):
+            item = ds[i]
+            assert item["metadata"]["task_type"] == task_type
+            score = ds.score_answer(item["answer"], item)
+            assert score >= 1.0, f"Task {task_type}, item {i}: oracle scored {score}"