fix(docs): correct citations for coh and ccorr metrics

- coh: replace incorrectly cited Nolte 2004 (about ImCoh) with
  the actual reference used in Coh class: Nunez & Srinivasan (2006)
- ccorr: replace Berens 2009 (MATLAB toolbox) with the original
  statistical reference used in CCorr class: Fisher (1995)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: Ramdam17

hypyp/analyses.py

@@ -490,14 +490,14 @@ def compute_sync(complex_signal: np.ndarray, mode: str, epochs_average: bool = T
         Lachaux et al. (1999). *Human Brain Mapping*, 8(4), 194–208.
     'ccorr' : Circular Correlation
         Circular analogue of Pearson r using global circular mean centering.
-        Berens (2009). *Journal of Statistical Software*, 31(10), 1–21.
+        Fisher, N. I. (1995). *Statistical analysis of circular data*. Cambridge University Press.
     'accorr' : Adjusted Circular Correlation
         Same as ccorr but with per-pair phase centering for improved accuracy.
         Supports ``optimization`` parameter (numba / torch).
         Zimmermann et al. (2024). *Imaging Neuroscience*, 2.
     'coh' : Coherence
         ``Coh = |⟨S₁₂⟩|² / (⟨|S₁|²⟩·⟨|S₂|²⟩)`` — ranges [0, 1].
-        Nolte et al. (2004). *Clinical Neurophysiology*, 115(10), 2292–2307.
+        Nunez, P. L., & Srinivasan, R. (2006). *Electric fields of the brain*. Oxford University Press.
     'imaginary_coh' / 'imcoh' : Imaginary Coherence
         ``Im(⟨S₁₂⟩) / √(⟨|S₁|²⟩·⟨|S₂|²⟩)`` — volume-conduction resistant.
         Nolte et al. (2004). *Clinical Neurophysiology*, 115(10), 2292–2307.

hypyp/sync/README.md

@@ -34,9 +34,8 @@ CCorr = Σ sin(α₁ − ᾱ₁) · sin(α₂ − ᾱ₂)
 ```
 where `ᾱ` is the circular mean of `α`.
 
-**Reference:** Berens, P. (2009). CircStat: A MATLAB toolbox for circular statistics.
-*Journal of Statistical Software*, 31(10), 1–21.
-https://doi.org/10.18637/jss.v031.i10
+**Reference:** Fisher, N. I. (1995). *Statistical analysis of circular data*.
+Cambridge University Press.
 
 ---
 
@@ -76,9 +75,8 @@ where `S₁₂(f)` is the cross-spectrum.
 
 Ranges from 0 to 1. Sensitive to volume conduction.
 
-**Reference:** Nolte, G., et al. (2004). Identifying true brain interaction from EEG
-data using the imaginary part of coherency. *Clinical Neurophysiology*, 115(10), 2292–2307.
-https://doi.org/10.1016/j.clinph.2004.04.029
+**Reference:** Nunez, P. L., & Srinivasan, R. (2006). *Electric fields of the brain:
+the neurophysics of EEG*. Oxford University Press.
 
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