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) <arXiv:0711.1146> 
        for details on the model.

Matrix algebra using the 'Eigen' C++ library: determinant,
    rank, inverse, pseudo-inverse, kernel and image, QR decomposition,
    Cholesky decomposition, Schur decomposition, Hessenberg decomposition, 
    linear least-squares problems. Also provides matrix functions such as 
    exponential, logarithm, power, sine and cosine. Complex matrices are 
    supported.

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.

Methods and utilities for causal emergence.
    Used to explore and compute various information theory metrics for networks, such as effective information, effectiveness and causal emergence.

The summation notation suggested by Einstein (1916) <doi:10.1002/andp.19163540702> is a 
  concise mathematical notation that implicitly sums over repeated indices of n-dimensional arrays. Many ordinary
  matrix operations (e.g. transpose, matrix multiplication, scalar product, 'diag()', trace etc.)
  can be written using Einstein notation. The notation is particularly convenient for 
  expressing operations on arrays with more than two dimensions because the 
  respective operators ('tensor products') might not have a standardized name.

Provides EIOPA (European Insurance And Occupational Pensions Authority) risk-free rates. Please note that the author of this package is not affiliated with EIOPA. The data is accessed through a REST API available at <https://mehdiechchelh.com/api/>.