Package:
missForest
Authors:
Daniel J. Stekhoven
Category:
Single Imputation
Use-Cases:
Single Imputation of continuous and/or categorical data.
Popularity:
Description:
The function ‘missForest’ in this package is used to impute missing values particularly in the case of mixed-type data. It uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation. It can be run in parallel to save computation time.
Read more →Package:
simputation
Authors:
Mark van der Loo [aut, cre]
Category:
Single Imputation, Meta-Package
Use-Cases:
Use imputation algortihms of multiple packages via one interface.
Popularity:
Description:
Easy to use interfaces to a number of imputation methods that fit in the not-a-pipe operator of the ‘magrittr’ package.
Last update:
Algorithms:
- impute_cart Decision Tree Imputation
- impute_const Impute by variable derivation
- impute_em Multivariate, model-based imputation
- impute_en (Robust) Linear Regression Imputation
- impute_hotdeck Hot deck imputation
- impute_knn Hot deck imputation
- impute_lm (Robust) Linear Regression Imputation
- impute_median Impute (group-wise) medians
- impute_mf Multivariate, model-based imputation
- impute_multivariate Multivariate, model-based imputation
- impute_pmm Hot deck imputation
- impute_proxy Impute by variable derivation
- impute_rf Decision Tree Imputation
- impute_rhd Hot deck imputation
- impute_rlm (Robust) Linear Regression Imputation
- impute_shd Hot deck imputation
Datasets:
none
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