Estimation of the parameters in a model for
        symmetric relational data (e.g., the above-diagonal part of a
        square matrix), using a model-based eigenvalue decomposition
        and regression. Missing data is accommodated, and a posterior
        mean for missing data is calculated under the assumption that
        the data are missing at random. The marginal distribution of
        the relational data can be arbitrary, and is fit with an
        ordered probit specification. See Hoff (2007) <doi:10.48550/arXiv.0711.1146>.
        for details on the model.

Estimates RxC transfer matrices from aggregated marginal data using
             a two-stage (GME+IPF) information-theoretic approach within a two-step 
             (global+local) estimation procedure. The resulting matrices are consistent 
             with observed row and column marginals across collections of subtables 
             (e.g. precincts, polling stations, or districts).
   References:
   Golan, A., Judge, G., & Miller, D. (1996). Maximum Entropy Econometrics: Robust Estimation with Limited Data. Wiley.
   Judge, G., Miller, D.J., & Cho, W.K.T. (2004). An information theoretic approach to ecological estimation and inference. In G. King, O. Rosen, & M. A. Tanner (Eds.), Ecological Inference: New Methodological Strategies (pp. 162–187). Cambridge University Press.
   Mittelhammer, R., Judge, G., & Miller, D. (2000). Econometric Foundations. Cambridge University Press.
   Pavia, J.M. (2023) <doi:10.1007/s43545-023-00658-y>
   Acknowledgements: The author wish to thank Conselleria de Economia, Hacienda y Administracion Publica (grant CIACIO/2023/031) for supporting this research.

Efficiently impute large scale matrix with missing values via its unbiased low-rank matrix approximation. Our main approach is Hard-Impute algorithm proposed in <https://www.jmlr.org/papers/v11/mazumder10a.html>, which achieves highly computational advantage by truncated singular-value decomposition.

Perform tensor operations using a concise yet expressive syntax inspired by the Python library of the same name.
    Reshape, rearrange, and combine multidimensional arrays for scientific computing, machine learning, and data analysis.
    Einops simplifies complex manipulations, making code more maintainable and intuitive.
    The original implementation is demonstrated in Rogozhnikov (2022) <https://openreview.net/forum?id=oapKSVM2bcj>.