Packaged: 2018-08-22 16:29:20 UTC; rwoldford

Title: Efficiently Impute Large Scale Incomplete Matrix
person(given = "Zhe", family = "Gao", email = "gaozh8@mail2.sysu.edu.cn", role = c("aut", "cre")),
person(given = "Xueqin", family = "Wang", email = "wangxq88@mail.sysu.edu.cn", role = c("aut")),
person(given = "Yixuan", family = "Qiu", email = "yixuan.qiu@cos.name", role = c("cph")),
person(given = "Gael", family = "Guennebaud", email = "gael.guennebaud@inria.fr", role = c("cph", "ctb")),
person(given = "Jitse", family = "Niesen", email = "jitse@maths.leeds.ac.uk", role = c("cph", "ctb")),
person(given = "Ray", family = "Gardner", role = c("ctb")))
Description: Efficiently impute large scale matrix with missing values via its unbiased low-rank matrix approximation. Our main approach is Hard-Impute algorithm proposed in <http://www.jmlr.org/papers/v11/mazumder10a.html>, which achieves highly computational advantage by truncated singular-value decomposition.

Description: 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.

Title: Effective Information and Causal Emergence
person("Travis", "Byrum", email = "tbyrum@terpmail.umd.edu",
person("Anshuman", "Swain", email = "answain@terpmail.umd.edu",
person("Brennan", "Klein", email = "klein.br@northeastern.edu",
person("William", "Fagan", email = "bfagan@umd.edu",
Description: 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.
Imports: assertthat, igraph, magrittr, shiny, entropy
Suggests: testthat, RColorBrewer, knitr, rmarkdown, bench
Packaged: 2020-04-20 08:27:09 UTC; travisbyrum

Authors@R: person("Constantin", "Ahlmann-Eltze", email = "artjom31415@googlemail.com",
Description: 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.
Author: Constantin Ahlmann-Eltze [aut, cre]