feat(eda): series temporales + rigor anti-data-mining + PDF movil + /eda + benchmark issues

Bloque del grupo eda (sesion ausente EDA-benchmark):
- 8 funciones nuevas: adf_kpss_stationarity, acf_pacf, stl_decompose, to_returns,
  fdr_correction, suggest_reexpression, exploratory_caveats, render_eda_pdf
- integracion: profile_table (run_series, emit_pdf), association_matrix (FDR Benjamini-Hochberg),
  render_eda_markdown (secciones series/reexpresion/caveats)
- slash commands /eda y /capitulos
- issues 0173-0177: mejoras del /eda derivadas del benchmark sobre 12 datasets reales
  (outlier_pct x100, periodo estacional, FK inference, render models, tipos id-like)

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

python/functions/datascience/__init__.py

@@ -45,9 +45,25 @@ from .run_eda_models import run_eda_models
 from .eda_llm_insights import eda_llm_insights
 from .build_eda_notebook import build_eda_notebook
 from .decode_qr_image import decode_qr_image
+from .adf_kpss_stationarity import adf_kpss_stationarity
+from .acf_pacf import acf_pacf
+from .stl_decompose import stl_decompose
+from .to_returns import to_returns
+from .fdr_correction import fdr_correction
+from .suggest_reexpression import suggest_reexpression
+from .exploratory_caveats import exploratory_caveats
+from .render_eda_pdf import render_eda_pdf
 
 __all__ = [
     "decode_qr_image",
+    "adf_kpss_stationarity",
+    "acf_pacf",
+    "stl_decompose",
+    "to_returns",
+    "fdr_correction",
+    "suggest_reexpression",
+    "exploratory_caveats",
+    "render_eda_pdf",
     "summarize_table_duckdb",
     "summarize_table_pg",
     "spearman_corr",