R package yaImpute: Nearest Neighbor Observation Imputation and Evaluation Tools. Performs nearest neighbor-based imputation using one or more alternative approaches to processing multivariate data. These include methods based on canonical correlation analysis, canonical correspondence analysis, and a multivariate adaptation of the random forest classification and regression techniques of Leo Breiman and Adele Cutler. Additional methods are also offered. The package includes functions for comparing the results from running alternative techniques, detecting imputation targets that are notably distant from reference observations, detecting and correcting for bias, bootstrapping and building ensemble imputations, and mapping results.
Keywords for this software
References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Kocheturov, Anton; Pardalos, Panos M.; Karakitsiou, Athanasia: Massive datasets and machine learning for computational biomedicine: trends and challenges (2019)
- Trabelsi, Asma; Elouedi, Zied; Lefevre, Eric: Decision tree classifiers for evidential attribute values and class labels (2019)
- Imbert, Alyssa; Vialaneix, Nathalie: Exploring, handling, imputing and evaluating missing data in statistical analyses: a review of existing approaches (2018)
- Alexander Kowarik; Matthias Templ: Imputation with the R Package VIM (2016) not zbMATH
- Biau, Gérard; Scornet, Erwan: A random forest guided tour (2016)
- Nicholas Crookston; Andrew Finley: yaImpute: An R Package for kNN Imputation (2008) not zbMATH