Title: Propensity Score Weighting for Causal Inference
Authors@R: c(person("Tianhui","Zhou", role = c("aut", "cre"),
Description: Supports propensity score weighting analysis of observational studies and randomized trials. Enables the estimation and inference of average causal effects among target populations with binary and multiple treatments using the methods developed in Li, Morgan and Zaslavsky (2018) <doi:10.1080/01621459.2016.1260466> and Li and Li (2019) <doi:10.1214/19-AOAS1282>.

Title: Propensity Score Weighting for Causal Inference with
Observational Studies and Randomized Trials
Description: Supports propensity score weighting analysis of observational studies and randomized trials. Enables the estimation and inference of average causal effects with binary and multiple treatments using overlap weights (ATO), inverse probability of treatment weights (ATE), average treatment effect among the treated weights (ATT), matching weights (ATM) and entropy weights (ATEN), with and without propensity score trimming. These weights are members of the family of balancing weights introduced in Li, Morgan and Zaslavsky (2018) <doi:10.1080/01621459.2016.1260466> and Li and Li (2019) <doi:10.1214/19-AOAS1282>.

Imports: nnet, MASS, ggplot2, numDeriv, gbm, SuperLearner

Imports: lme4, nnet, MASS, ggplot2, numDeriv, gbm, SuperLearner

Package: PTAk
Version: 1.1-1
Date: 2001/04/17
Title: Principal Tensor Analysis on k modes
Author: Didier Leibovici <didier@fmrib.ox.ac.uk>.
Depends: tensor (useful but not necessary)
Description: 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.
License: free for non-commercial purposes.
Maintainer: Didier Leibovici <didier@fmrib.ox.ac.uk>.
URL: http://www.fmrib.ox.ac.uk/~didier

as a generalisation of SVD also supporting non-identity metrics and penalisations.