Contains functions for multiple imputation which
complements existing functionality in R.
In particular, several imputation methods for the
mice package (van Buuren & Groothuis-Oudshoorn, 2011,
<doi:10.18637/jss.v045.i03>) are included.
Main features of the miceadds package include
plausible value imputation (Mislevy, 1991,
<doi:10.1007/BF02294457>), multilevel imputation for
variables at any level or with any number of hierarchical
and non-hierarchical levels (Grund, Luedtke & Robitzsch,
2018, <doi:10.1177/1094428117703686>; van Buuren, 2018,
Ch.7, <doi:10.1201/9780429492259>), imputation using
partial least squares (PLS) for high dimensional
predictors (Robitzsch, Pham & Yanagida, 2016),
nested multiple imputation (Rubin, 2003,
<doi:10.1111/1467-9574.00217>) and substantive model
compatible imputation (Bartlett et al., 2015,
<doi:10.1177/0962280214521348>).
CDM, graphics, lme4, methods, mitools, Rcpp, sirt, stats, TAM,
utils
BIFIEsurvey, blme, car, coda, foreign, grouped, inline, MASS,
MBESS, MCMCglmm, mdmb, multiwayvcov, numDeriv, readxl,
sjlabelled

BIFIEsurvey, blme, car, CDM, coda, foreign, grouped, inline,
lme4, MASS, MBESS, MCMCglmm, mdmb, numDeriv, readxl, sandwich,
sirt, sjlabelled, TAM

Alexander Robitzsch [aut, cre], Simon Grund [aut],
Thorsten Henke [ctb]

BIFIEsurvey, blme, car, CDM, coda, foreign, grouped, imputeR,
inline, lme4, MASS, MBESS, MCMCglmm, mdmb, numDeriv, readxl,
sandwich, simputation, sirt, sjlabelled, TAM

foreign (>= 0.8-80), knitr, rmarkdown, testthat (>= 3.0.0),
bookdown, readr
Data and Statistical Analyses after Multiple Imputation
c(
person("Martijn", "Heymans", email = "mw.heymans@amsterdamumc.nl", role=c("cre", "aut"),
comment = c(ORCID = "0000-0002-3889-0921")),
person("Jaap", "Brand", email = "jaapbrand2651965@gmail.com", role=c("ctb")))
Statistical Analyses and Pooling after Multiple Imputation. A large variety
of repeated statistical analysis can be performed and finally pooled. Statistical analysis
that are available are, among others, Levene's test, Odds and Risk Ratios, One sample
proportions, difference between proportions and linear and logistic regression models.
Functions can also be used in combination with the Pipe operator.
More and more statistical analyses and pooling functions will be added over time.
Heymans (2007) <doi:10.1186/1471-2288-7-33>.
Eekhout (2017) <doi:10.1186/s12874-017-0404-7>.
Wiel (2009) <doi:10.1093/biostatistics/kxp011>.
Marshall (2009) <doi:10.1186/1471-2288-9-57>.
Sidi (2021) <doi:10.1080/00031305.2021.1898468>.
Lott (2018) <doi:10.1080/00031305.2018.1473796>.
Grund (2021) <doi:10.31234/osf.io/d459g>.
Martijn Heymans [cre, aut] (<https://orcid.org/0000-0002-3889-0921>),
Jaap Brand [ctb]