Package:
mixgb
Authors:
Yongshi Deng [aut, cre], Thomas Lumley [ths]
Category:
Single Imputation, Analysis for Incomplete Data
Use-Cases:
Multiple imputation for large datasets.
Popularity:
Description:
Multiple imputation using ‘XGBoost’, subsampling, and predictive mean matching as described in Deng and Lumley (2023) doi:10.1080/10618600.2023.2252501. The package supports various types of variables, offers flexible settings, and enables saving an imputation model to impute new data. Data processing and memory usage have been optimised to speed up the imputation process.
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rrcovNA
Authors:
Valentin Todorov [aut, cre]
Category:
Single Imputation, Analysis for Incomplete Data
Use-Cases:
High-breakdown point estimation for location and scatter, Suitable for incomplete and outlier-contaminated data.
Popularity:
Description:
Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, simulation, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis.
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