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missMethods - Methods for Missing Data

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>.

Last updated 2 years ago

6.95 score 7 stars 4 packages 107 scripts 732 downloads

imputeGeneric - Ease the Implementation of Imputation Methods

The general workflow of most imputation methods is quite similar. The aim of this package is to provide parts of this general workflow to make the implementation of imputation methods easier. The heart of an imputation method is normally the used model. These models can be defined using the 'parsnip' package or customized specifications. The rest of an imputation method are more technical specification e.g. which columns and rows should be used for imputation and in which order. These technical specifications can be set inside the imputation functions.

Last updated 3 years ago

2.70 score 1 stars 2 scripts 141 downloads