person("Tidylab", role = c("cph", "fnd"))
Suggests: testthat, collections, dplyr, DBI, RSQLite, ggplot2, tibble,
Author: Harel Lustiger [aut, cre] (<https://orcid.org/0000-0003-2953-9598>),

Suggests: testthat, DBI, RSQLite, ggplot2
Imports: collections, dplyr, purrr, stringr, R6, tibble, tidyr

person("Harel", "Lustiger", , "tidylab@gmail.com", role = c("aut", "cre"),
Description: Build robust and maintainable software with object-oriented
design patterns in R. Design patterns abstract and present in neat,
well-defined components and interfaces the experience of many software
designers and architects over many years of solving similar problems.
These are solutions that have withstood the test of time with respect
to re-usability, flexibility, and maintainability. 'R6P' provides
abstract base classes with examples for a few known design patterns.
The patterns were selected by their applicability to analytic projects
in R. Using these patterns in R projects have proven effective in
dealing with the complexity that data-driven applications possess.
Imports: collections, dplyr, purrr, R6, stringr, tibble, tidyr
Suggests: DBI, ggplot2, RSQLite, testthat (>= 3.0.0)

Title: R6 Class for Structural Causal Models
Authors@R: person("Juha", "Karvanen", email = "juha.karvanen@iki.fi",
Description: The implemented R6 class 'SCM' aims to simplify working with structural causal models. The missing data mechanism can be defined as a part of the structural model. The class contains methods for 1) defining a structural causal model via functions, text or conditional probability tables, 2) printing basic information on the model, 3) plotting the graph for the model using packages 'igraph' or 'qgraph', 4) simulating data from the model, 5) applying an intervention, 6) checking the identifiability of a query using the R packages 'causaleffect' and 'dosearch', 7) defining the missing data mechanism, 8) simulating incomplete data from the model according to the specified missing data mechanism and 9) checking the identifiability in a missing data problem using the R package 'dosearch'. In addition, there are functions for running experiments and doing counterfactual inference using simulation.
Imports: causaleffect, data.table, dosearch, igraph, R6, stats
Suggests: rmarkdown, knitr, qgraph, sqldf
Author: Juha Karvanen [aut, cre] (<https://orcid.org/0000-0001-5530-769X>)