knitr, lifecycle, rmarkdown, testthat (>= 3.0.0), usethis

bibtex (>= 0.5.0), knitr, lifecycle, rmarkdown, testthat (>=
3.0.0), usethis

Identification of Counterfactual Queries in Causal Models
c(
person(given = "Santtu",
family = "Tikka",
role = c("aut", "cre"),
email = "santtuth@gmail.com",
comment = c(ORCID = "0000-0003-4039-4342")))
Facilitates the identification of counterfactual queries in
structural causal models via the ID* and IDC* algorithms
by Shpitser, I. and Pearl, J. (2008)
<https://jmlr.org/papers/v9/shpitser08a.html>. Provides a simple interface
for defining causal graphs and counterfactual conjunctions. Construction
of parallel worlds graphs and counterfactual graphs is done automatically
based on the counterfactual query and the causal graph.
covr, dagitty, igraph, mockery, testthat (>= 3.0.0)

Facilitates the identification of counterfactual queries in
structural causal models via the ID* and IDC* algorithms
by Shpitser, I. and Pearl, J. (2007, 2008)
<arXiv:1206.5294>, <https://jmlr.org/papers/v9/shpitser08a.html>.
Provides a simple interface for defining causal diagrams and counterfactual
conjunctions. Construction of parallel worlds graphs and counterfactual graphs
is carried out automatically based on the counterfactual query and the causal
diagram.

Yi Zhao <zhaoyi1026@gmail.com>, Xi Luo <xi.rossi.luo@gmail.com>, Martin Lindquist <mal2053@gmail.com>, Brian Caffo <bcaffo@gmail.com>
Performs causal functional mediation analysis (CFMA) for functional treatment, functional mediator, and functional outcome. This package includes two functional mediation model types: (1) a concurrent mediation model and (2) a historical influence mediation model. See Zhao et al. (2018), Functional Mediation Analysis with an Application to Functional Magnetic Resonance Imaging Data, <arXiv:1805.06923> for details.

Cystic Fibrosis Survival Prediction Model Based on Stanojevic
Model
c(
person("Sanja", "Stanojevic", email = "sanja.stanojevic@sickkids.ca", role = c("aut", "cph")),
person("Jenna", "Sykes", email = "SykesJ@smh.ca", role = c("aut", "cph")),
person("George A.", "Whitmore", email = "george.whitmore@mcgill.ca", role = c("aut", "cph")),
person("Shawn D.", "Aaron", email = "saaron@toh.ca", role = c("aut", "cph")),
person("Aida", "Kazemi", email = "aidakazemi10@gmail.com", role = c("aut")),
person("Amin", "Adibi", email = "adibi@alumni.ubc.ca", role = c("aut", "cre")))
Allows clinicians to predict survival probabilities over the next two years for cystic fibrosis patients, based on the clinical prediction models published in Stanojevic et al. (2019) <doi:10.1183/13993003.00224-2019>.
Sanja Stanojevic [aut, cph],
Jenna Sykes [aut, cph],
George A. Whitmore [aut, cph],
Shawn D. Aaron [aut, cph],
Aida Kazemi [aut],
Amin Adibi [aut, cre]