Handling missing values with/in multivariate data analysis
(principal component methods)
Francois Husson <husson@agrocampus-ouest.fr>, Julie Josse
<josse@agrocampus-ouest.fr>
Imputation of incomplete continuous or categorical
datasets; Missing values are imputed with a principal component
analysis (PCA) or a multiple correspondence analysis (MCA)
model; Perform multiple imputation with and in PCA

Handling Missing Values with Multivariate Data Analysis
Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA.

Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA.

Imputation of incomplete continuous or categorical
datasets; Missing values are imputed with a principal component
analysis (PCA), a multiple correspondence analysis (MCA) model
or a multiple factor analysis (MFA) model; Perform multiple
imputation with and in PCA

Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA

R (>= 3.0.0), FactoMineR (>= 1.30),
graphics,grDevices,mice,mvtnorm,stats,utils

person(given = "Tobias",
family = "Rockel",
role = c("aut", "cre"),
email = "Rockel.To@gmail.com")
Supply functions for the creation and handling of missing data
as well as tools to evaluate missing data methods. Nearly all possibilities
of generating missing data discussed by Santos et. al (2019)
<doi:10.1109/ACCESS.2019.2891360> and some additional are implemented.
Functions are supplied to compare parameter estimates and imputed values to
true values to evaluate missing data methods. Evaluations of these types
are done, for example, by Cetin-Berber et al. (2019)
<doi:10.1177/0013164418805532>  and Kim et al. (2005)
<doi:10.1093/bioinformatics/bth499>.
ggplot2, knitr, lpSolve, rmarkdown, testthat (>= 2.1.0),
tibble