Suggests: R2jags, R2OpenBUGS, ggplot2, grid, INLA, splancs, mgcv, ldr,

shiny, shinythemes, knitr, rmarkdown, plotly

Imports: dplyr, ggplot2, GrassmannOptim, gridExtra, graphics, MASS,
Suggests: coda, grid, INLA, knitr, markdown, Matrix, MCMCvis, mgcv,
plotly, rmarkdown, rstan, RColorBrewer, R2jags, splancs,
Description: Produces an economic evaluation of a sample of suitable variables of
cost and effectiveness / utility for two or more interventions,
e.g. from a Bayesian model in the form of MCMC simulations.
This package computes the most cost-effective alternative and
produces graphical summaries and probabilistic sensitivity analysis.

Imports: cli (>= 3.3.0), dplyr, ggplot2, gridExtra, graphics, MASS,
Matrix, purrr, Rdpack, reshape2, rlang, scales
Suggests: coda, grid, INLA, knitr, markdown, MCMCvis, mgcv, plotly,
rjags, rmarkdown, rstan, RColorBrewer, splancs, testthat (>=
produces graphical summaries and probabilistic sensitivity analysis,
Author: Gianluca Baio [aut, cre] (<https://orcid.org/0000-0003-4314-2570>),

Title: The Bayesian Causal Effect Estimation Algorithm
Author: Denis Talbot, Genevi�ve Lefebvre, Juli Atherton.
Description: Implementation of the Bayesian Causal Effect Estimation algorithm,
a data-driven method for the estimation of the causal effect of a continuous
exposure on a continuous outcome. For more details, see Talbot et al. (2015).

a data-driven method for the estimation of the causal effect of an exposure
on a continuous outcome. For more details, see Talbot et al. (2015) DOI:10.1515/jci-2014-0035.

Author: Denis Talbot, Genevi�ve Lefebvre, Juli Atherton, Yohann Chiu.

Description: A Bayesian model averaging approach to causal effect estimation
based on the BCEE algorithm. Currently supports binary or continuous
exposures and outcomes. For more details, see
Talbot and Beaudoin (2020) <arXiv:2003.11588>.
Packaged: 2020-04-01 17:15:22 UTC; Denis Talbot

Title: Bayesian models for cost-effectiveness analysis in the presence
Description: Implements a full Bayesian cost-effectiveness analysis in the case where the cost variable is characterised by structural zeros. The package implements the Gamma, log-Normal and Normal models for the cost variable and the Gamma, Beta, Bernoulli and Normal models for the measure of clinical effectiveness.