A simple general method for oblique rotation

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Critical success factors for the sustainability of Kaizen event human resource outcomes: An empirical study (2011, methodology): “All factor analyses were conducted with principal components extraction, and an oblique rotation method was used because theory suggested that the survey scales may be correlated (Jennrich, 2002; Johnson and Wichern, 2007).”
Empirical orthogonal functions and related techniques in atmospheric science: A review (2007, methodology): “…to changes in the number of variables (Richman, 1986), although the difference in practice is not significant. b. Oblique rotation Here the rotation matrix R is chosen to be nonorthogonal (Harman, 1976; Kiers, 1994; Jennrich, 2002) normalised to have unit-length columns, and where R = (RT )−1.”
Gradient Projection Algorithms and Software for Arbitrary Rotation Criteria in Factor Analysis (2005, background): “More recently, Jennrich (2001, 2002) factor analysis applications.”
Rotated Word Vector Representations and their Interpretability (2017, methodology): “…be an orthogonal matrix that with m ( m − 1) / 2 constraints, satisﬁes: Oblique rotations allow the correlation between dimension to be non-zero, resulting in m constraints satisfying: The solution for the input matrix is computed by using the gradient projection algorithm (Jennrich, 2001, 2002).”