Title: Accelerated Bayesian Analytics with DAGs
Authors@R: c(person("Adam", "Fleischhacker", email = "ajf@udel.edu", role = c("aut", "cre","cph")),
person("Daniela", "Dapena", role = "ctb"),
Description: Accelerate Bayesian analytics workflows in 'R' through interactive modelling,
visualization, and inference. Define probabilistic graphical models using directed
acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians,
and programmers. This package relies on the sleek and elegant 'greta' package for
Bayesian inference. 'greta', in turn, is an interface into 'TensorFlow' from 'R'. See
<http://github.com/flyaflya/causact> or <http://causact.com> for more documentation.
SystemRequirements: Python and TensorFlow are needed for Bayesian
inference computations; Python (>= 2.7.0) with header files and
Imports: DiagrammeR (>= 1.0.6), dplyr (>= 0.8.5), magrittr (>= 1.5),
Author: Adam Fleischhacker [aut, cre, cph],

Bayesian inference. 'greta', in turn, is an interface into 'TensorFlow' from 'R'.
Install 'greta' using instructions available here: <http://causact.com/install-greta.html>.
See <http://github.com/flyaflya/causact> or <http://causact.com> for more documentation.
https://www.tensorflow.org/); TensorFlow Probability (= v0.7.0;

Install 'greta' using instructions available here: <http://www.causact.com/install-tensorflow-greta-and-causact.html>.

Install 'greta' using instructions available here: <https://www.causact.com/install-tensorflow-greta-and-causact.html>.
See <https://github.com/flyaflya/causact> or <https://www.causact.com/> for more documentation.

Imports: DiagrammeR (>= 1.0.7), dplyr (>= 0.8.5), magrittr (>= 1.5),

0.5.0), htmlwidgets (>= 1.5.1), rstudioapi (>= 0.11), lifecycle
Suggests: knitr, covr, testthat (>= 3.0.0), rmarkdown

Authors@R: c(person("Suyeon", "Kang", role = c("aut", "cre"),
Imports: stats, parallel, MASS, nnet, SuppDists, CBPS, PSweight,
Description: We implement causal decomposition analysis using the methods proposed by Park, Lee, and Qin (2020) <arXiv:2008.12812> and Park, Kang, and Lee (2021+). This package allows researchers to use the multiple-mediator-imputation, single-mediator-imputation, and product-of-coefficients regression methods to estimate the initial disparity, disparity reduction, and disparity remaining. It also allows to make the inference conditional on baseline covariates.
Packaged: 2021-09-11 05:14:28 UTC; Suyeon Kang