diff --git a/xeofs/models/cca.py b/xeofs/models/cca.py
index 0d3029e..a32d9aa 100644
--- a/xeofs/models/cca.py
+++ b/xeofs/models/cca.py
@@ -218,13 +218,11 @@ def _fit_algorithm(self, views: List[DataArray]) -> Self:
 
 
 class CCA(CCABaseModel):
-    r"""Canonical Correlation Analysis (CCA) model.
+    r"""Canonical Correlation Analysis.
     
-    Regularised CCA (canonical ridge) model. 
-    
-    CCA identifies linear combinations of variables from multiple datasets that 
-    maximize their mutual correlations. An optional regularisation parameter can be used to
-    improve the conditioning of the covariance matrix.
+    Canonical Correlation Analysis (CCA) identifies linear combinations of variables from multiple datasets that 
+    maximize their mutual correlations. An optional regularisation parameter (ridge regression)
+    can be used to improve the conditioning of the covariance matrix.
 
     The objective function of (regularised) CCA is:
 
diff --git a/xeofs/models/eof.py b/xeofs/models/eof.py
index 3f6cfa7..367b8cc 100644
--- a/xeofs/models/eof.py
+++ b/xeofs/models/eof.py
@@ -11,9 +11,10 @@
 
 
 class EOF(_BaseModel):
-    """Empirical Orthogonal Functions (EOF) analysis.
+    """EOF analysis.
 
-    More commonly known as Principal Component Analysis (PCA).
+    Empirical Orthogonal Functions (EOF) analysis, more commonly known
+    as Principal Component Analysis (PCA).
 
     Parameters
     ----------
@@ -236,7 +237,7 @@ def explained_variance_ratio(self) -> DataArray:
 
 
 class ComplexEOF(EOF):
-    """Complex Empirical Orthogonal Functions (Complex EOF) analysis.
+    """Complex EOF analysis.
 
     The Complex EOF analysis [1]_ [2]_ [3]_ [4]_ (also known as Hilbert EOF analysis) applies a Hilbert transform
     to the data before performing the standard EOF analysis.
diff --git a/xeofs/models/gwpca.py b/xeofs/models/gwpca.py
index 1293077..4d317c6 100644
--- a/xeofs/models/gwpca.py
+++ b/xeofs/models/gwpca.py
@@ -26,9 +26,9 @@
 
 
 class GWPCA(_BaseModel):
-    """Geographically weighted PCA (GWPCA).
+    """Geographically weighted PCA.
 
-    GWPCA [1]_ uses a geographically weighted approach to perform PCA for
+    Geographically weighted PCA (GWPCA) [1]_ uses a geographically weighted approach to perform PCA for
     each observation in the dataset based on its local neighbors.
 
     The neighbors for each observation are determined based on the provided
diff --git a/xeofs/models/mca.py b/xeofs/models/mca.py
index 8d97b73..c2ebebc 100644
--- a/xeofs/models/mca.py
+++ b/xeofs/models/mca.py
@@ -13,7 +13,7 @@
 
 
 class MCA(_BaseCrossModel):
-    """Maximum Covariance Analyis (MCA).
+    """Maximum Covariance Analyis.
 
     MCA is a statistical method that finds patterns of maximum covariance between two datasets.
 
@@ -546,7 +546,7 @@ def heterogeneous_patterns(self, correction=None, alpha=0.05):
 
 
 class ComplexMCA(MCA):
-    """Complex Maximum Covariance Analysis (MCA).
+    """Complex MCA.
 
     Complex MCA, also referred to as Analytical SVD (ASVD) by Elipot et al. (2017) [1]_,
     enhances traditional MCA by accommodating both amplitude and phase information.
diff --git a/xeofs/models/opa.py b/xeofs/models/opa.py
index 6db9916..3bb1189 100644
--- a/xeofs/models/opa.py
+++ b/xeofs/models/opa.py
@@ -11,13 +11,11 @@
 
 
 class OPA(_BaseModel):
-    """Optimal Persistence Analysis (OPA).
+    """Optimal Persistence Analysis.
 
-    OPA identifies the optimal persistence patterns or
-    optimally persistent patterns (OPP) with the
-    largest decorrelation time in a time-varying field. Introduced by DelSole
-    in 2001 [1]_, and further developed in 2006 [2]_, it's a method used to
-    find patterns whose time series show strong persistence over time.
+    Optimal Persistence Analysis (OPA) [1]_ [2]_ identifies the patterns with the
+    largest decorrelation time in a time-varying field, known as optimal
+    persistence patterns or optimally persistent patterns (OPP).
 
     Parameters
     ----------
@@ -46,8 +44,8 @@ class OPA(_BaseModel):
 
     References
     ----------
-    .. [1] DelSole, T., 2001. Optimally Persistent Patterns in Time-Varying Fields. Journal of the Atmospheric Sciences 58, 1341–1356. https://doi.org/10.1175/1520-0469(2001)058<1341:OPPITV>2.0.CO;2
-    .. [2] DelSole, T., 2006. Low-Frequency Variations of Surface Temperature in Observations and Simulations. Journal of Climate 19, 4487–4507. https://doi.org/10.1175/JCLI3879.1
+    .. [1] DelSole, T. Optimally Persistent Patterns in Time-Varying Fields. Journal of the Atmospheric Sciences 58, 1341–1356 (2001).
+    .. [2] DelSole, T. Low-Frequency Variations of Surface Temperature in Observations and Simulations. Journal of Climate 19, 4487–4507 (2006).
 
     Examples
     --------