Multiple Imputation in Causal Graph Discovery
c(
person(given = "Ronja", family = "Foraita", middle = NULL,
email = "foraita@leibniz-bips.de", role = c("aut", "cph", "cre"),
comment = c(ORCID = "0000-0003-2216-6653")),
person(given = "Janine", family = "Witte", middle = NULL,
email = "witte@leibniz-bips.de", role = "aut"))
Modified functions of the package 'pcalg' and some additional functions
to run the PC and the FCI (Fast Causal Inference) algorithm for constraint-based
causal discovery in incomplete and multiply imputed datasets.
Foraita R, Friemel J, Günther K, Behrens T, Bullerdiek J, Nimzyk R, Ahrens W, Didelez V (2020) <doi:10.1111/rssa.12565>;
Andrews RM, Foraita R, Didelez V, Witte J (2021) <arXiv:2108.13395>; Witte J, Foraita R, Didelez V (2022) <doi:10.1002/sim.9535>.
nnet, ranger, Rgraphviz, testthat (>= 3.0.0)
Ronja Foraita [aut, cph, cre] (<https://orcid.org/0000-0003-2216-6653>),
Janine Witte [aut]

Multivariate Imputation by Chained Equations
S. Van Buuren & C.G.M. Oudshoorn [R: peter.malewski@gmx.de]

Stef van Buuren <stef.vanbuuren@tno.nl> & Karin
Groothuis-Oudshoorn
Multiple imputation of multivariate incomplete data by
Fully Conditional Specification

Stef van Buuren <stef.vanbuuren@tno.nl> & Karin
Groothuis-Oudshoorn <k.groothuis@rrd.nl>
Multiple Imputation using Fully Conditional Specification

VIM, lattice, mitools, Zelig, nlme, lme4

Stef van Buuren <stef.vanbuuren@tno.nl> & Karin
Groothuis-Oudshoorn <c.g.m.oudshoorn@utwente.nl>
R (>= 2.4.0), MASS, nnet, methods, lattice, stringr
VIM, lattice, mitools, nlme, Zelig, lme4

Stef van Buuren <stef.vanbuuren@tno.nl> & Karin Groothuis-Oudshoorn <c.g.m.oudshoorn@utwente.nl>
R (>= 2.4.0), MASS, nnet, methods, lattice

R (>= 2.10), MASS, nnet, lattice, methods
VIM, mitools, nlme, Zelig, lme4, survival, gamlss

c(person("Stef", "van Buuren", role = c("aut","cre"), email
= "stef.vanbuuren@tno.nl"), person("Karin",
"Groothuis-Oudshoorn", role = "aut", email =
"c.g.m.oudshoorn@utwente.nl"), person("Alexander", "Robitzsch",
role = "ctb", email = "a.robitzsch@bifie.at"),
person("Gerko","Vink", role = "ctb", email = "g.vink@uu.nl"))
Stef van Buuren and Karin Groothuis-Oudshoorn, with
contributions from Alexander Robitzsch and Gerko Vink
AGD, mitools, nlme, Zelig, lme4, survival, gamlss, pan, VIM
Multiple imputation using Fully Conditional Specification
(FCS) implemented by the MICE algorithm. Each variable has its
own imputation model. Built-in imputation models are provided
for continuous data (predictive mean matching, normal), binary
data (logistic regression), unordered categorical data
(polytomous logistic regression) and ordered categorical data
(proportional odds). MICE can also impute continuous two-level
data (normal model, pan, second-level variables). Passive
imputation can be used to maintain consistency between
variables. Various diagnostic plots are available to inspect
the quality of the imputations.