nnet, MASS, ggplot2, numDeriv, gbm, SuperLearner

c(person("Tianhui","Zhou", role = c("aut", "cre"),
email = "thuizhou@outlook.com"),
person("Guangyu", "Tong", role = "aut",
email = "guangyu.tong@yale.edu"),
person("Fan", "Li", role = "aut",
email="fl35@duke.edu"),
person("Laine", "Thomas", role = "aut",
email="laine.thomas@duke.edu"),
person("Fan", "Li", role = "aut",
email = "fan.f.li@yale.edu"))
lme4, nnet, MASS, ggplot2, numDeriv, gbm, SuperLearner

A multiway method to decompose a tensor (array) of any order,
as a generalisation of SVD also supporting \emph{non-identity metrics} and \emph{penalisations}.
2-way SVD with these extensions is also available. The package includes also some other multiway
methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP with these extensions.

A multiway method to decompose a tensor (array) of any order,
as a generalisation of SVD also supporting non-identity metrics and penalisations.
2-way SVD with these extensions is also available. The package includes also some other multiway
methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP with these extensions.

free for non-commercial purposes (see LICENCE)

GPL+fees for commercial purposes (see COPYING)

Free for non-commercial purposes (see LICENSE)

A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD with these extensions is also available. The package includes also some other multiway methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP with these extensions.

A multiway method to decompose a tensor (array) of any
order, as a generalisation of SVD also supporting non-identity
metrics and penalisations. 2-way SVD with these extensions is
also available. The package includes also some other multiway
methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP with these
extensions.

A tool to draw inference in personalized medicine regimes

Adam Kapelner, Alina Levine & Justin Bleich
We provide inference for personalized medicine models. Namely, we answer the questions: (1) how much better does a purported personalized recommendation engine for treatments do over a business-as-usual approach and (2) is that difference statistically significant?
foreach, parallel, doParallel, graphics, grDevices, stats,
survival