Suggests: testthat, dplyr, tidyr, lubridate, SharpeR, sandwich,
'coerce.r' 'data.r' 'det.r' 'diag.r' 'eigen.r' 'elwise.r'

Collate: 'AllClass.r' 'bind.r' 'blockrep.r' 'coerce.r' 'data.r' 'det.r'
'diag.r' 'eigen.r' 'elwise.r' 'madness_pkg.r' 'matwise.r'
'max.r' 'norm.r' 'numderiv.r' 'Ops.r' 'reshape.r' 'solve.r'
'sum.r' 'sums.r' 'theta.r' 'to_objective.r' 'trace.r'

Title: Model Averaged Double Robust Estimation
Description: Estimates average treatment effects using model average double robust (MA-DR) estimation. The MA-DR estimator is defined as weighted average of double robust estimators, where each double robust estimator corresponds to a specific choice of the outcome model and the propensity score model. The MA-DR estimator extend the desirable double robustness property by achieving consistency under the much weaker assumption that either the true propensity score model or the true outcome model be within a specified, possibly large, class of models.

Title: May All Data be Reproducible and Transparent (MADRaT) *
person("Lavinia", "Baumstark", email = "lavinia@pik-potsdam.de", role = "aut"),
person("Anastasis", "Giannousakis", email = "giannou@pik-potsdam.de", role = "aut"),
person("Benjamin Leon", "Bodirsky", email = "bodirsky@pik-potsdam.de", role = "ctb"),
person("Ulrich", "Kreidenweis", email = "bodirsky@pik-potsdam.de", role = "ctb"))
Description: Provides a framework which should improve reproducibility and transparency in data processing. It provides functionality such as automatic meta data creation and management, rudimentary quality management, data caching, work-flow management and data aggregation.
* The title is a wish not a promise. By no means we expect this package to deliver everything what is needed to achieve full reproducibility and transparency, but we believe that it supports efforts in this direction.
Imports: spam, tools, utils, digest, methods, parallel, stringr
Suggests: igraph(>= 1.0.1), knitr, rmarkdown

person("Stephen", "Wirth", email = "wirth@pik-potsdam.de", role = "aut"),
person("Renato", "Rodrigues", email = "Renato.Rodrigues@pik-potsdam.de", role = "aut"),
person("Benjamin Leon", "Bodirsky", email = "bodirsky@pik-potsdam.de", role = "aut"),
person("Ulrich", "Kreidenweis", email = "kreidenweis@pik-potsdam.de", role = "aut"))
Imports: spam, tools, utils, digest, methods, parallel, stringr,
reshape2, stats, nnls, rlang, assertthat
Suggests: igraph(>= 1.0.1), knitr, rmarkdown, testthat, ggplot2