Methods for Missing Data
Apply a function for imputation
Count the number of NAs
Create MAR values using MAR1:x
Create MAR values using a censoring mechanism
Create MAR values by deleting values in one of two groups
Create MAR values using a ranking mechanism
Create MCAR values
Create MNAR values using MNAR1:x
Create MNAR values using a censoring mechanism
Create MNAR values by deleting values in one of two groups
Create MNAR values using a ranking mechanism
Evaluate estimated parameters after imputation
Evaluate imputed values
Evaluate estimated parameters
EM imputation
Impute expected values
Hot deck imputation in imputation classes
Impute in classes
LSimpute_adaptive
LSimpute_array
LSimpute_combined
LSimpute_gene
Mean imputation
Median imputation
Mode imputation
Simple random hot deck imputation
Median for ordered factors
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>.