Title: Simulate Forest Resources with the European Forestry Dynamics
Suggests: dplyr, ggplot2, gridExtra, knitr, rmarkdown, sf, testthat

Title: Efficient Least Squares for Total Causal Effects
Author: Richard Guo [aut, cre] (<https://orcid.org/0000-0002-2081-7398>)
person("Richard", "Guo", , "ricguo@uw.edu", role = c("aut", "cre"),
Description: Estimate a total causal effect from observational data under
linearity and causal sufficiency. The observational data is supposed to
be generated from a linear structural equation model (SEM) with independent
and additive noise. The underlying causal DAG associated the SEM is required
to be known up to a maximally oriented partially directed graph (MPDAG),
which is a general class of graphs consisting of both directed and
undirected edges, including CPDAGs (i.e., essential graphs) and DAGs. Such
graphs are usually obtained with structure learning algorithms with added
background knowledge. The program is able to estimate every identified
effect, including single and multiple treatment variables. Moreover, the
resulting estimate has the minimal asymptotic covariance (and hence
shortest confidence intervals) among all estimators that are based on the
Suggests: knitr, rmarkdown, testthat, qgraph

Title: Bayesian Effect Fusion for Categorical Predictors
Author: Daniela Pauger [aut, cre], Helga Wagner [aut], Gertraud Malsiner-Walli [aut]
Description: Variable selection and Bayesian effect fusion for categorical predictors in linear regression models. Effect fusion aims at the question which categories have a similar effect on the response and therefore can be fused to obtain a sparser representation of the model. Effect fusion and variable selection can be obtained either with a prior that has an interpretation as spike and slab prior on the level effect differences or with a sparse finite mixture prior on the level effects. The regression coefficients are estimated with a flat uninformative prior after model selection or model averaged. For posterior inference, an MCMC sampling scheme is used that involves only Gibbs sampling steps.
Imports: Matrix, MASS, bayesm, cluster, ggplot2, utils, stats