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/>.

Provides methods for analyzing R by C ecological contingency
        tables using the extreme case analysis, ecological regression,
        and Multinomial-Dirichlet ecological inference models.  Also
        provides tools for manipulating higher-dimension data objects.

Analysis of dichotomous and polytomous response data using the explanatory item response modeling framework, as described in Bulut, Gorgun, & Yildirim-Erbasli (2021) <doi:10.3390/psych3030023>, Stanke & Bulut (2019) <doi:10.21449/ijate.515085>, and De Boeck & Wilson (2004) <doi:10.1007/978-1-4757-3990-9>. Generalized linear mixed modeling is used for estimating the effects of item-related and person-related variables on dichotomous and polytomous item responses.