Package: missMethods 0.4.0

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>.

Authors:Tobias Rockel [aut, cre]

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missMethods.pdf |missMethods.html✨
missMethods/json (API)
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Bug tracker:https://github.com/torockel/missmethods/issues

On CRAN:

26 exports 7 stars 2.08 score 1 dependencies 4 dependents 82 scripts 655 downloads

Last updated 2 years agofrom:37e73366e2. Checks:OK: 7. Indexed: yes.

Exports:apply_imputationcount_NAdelete_MAR_1_to_xdelete_MAR_censoringdelete_MAR_one_groupdelete_MAR_rankdelete_MCARdelete_MNAR_1_to_xdelete_MNAR_censoringdelete_MNAR_one_groupdelete_MNAR_rankevaluate_imputation_parametersevaluate_imputed_valuesevaluate_parametersimpute_EMimpute_expected_valuesimpute_hot_deck_in_classesimpute_in_classesimpute_LS_adaptiveimpute_LS_arrayimpute_LS_combinedimpute_LS_geneimpute_meanimpute_medianimpute_modeimpute_sRHD

Dependencies:mvtnorm