Package:
Hmisc
Authors:
Harrell Miscellaneous, Charles Dupont
Category:
Single and Multiple Imputation
Use-Cases:
Data summarization and exploration, handling missing data and imputation, regression modeling and splines, correlation analysis and statistical tests, data visualization, variable labeling and metadata management, data manipulation and transformation.
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.
Algorithms:
- impute(): Allows simple imputation (mean, median, or mode) of missing values.
- transcan(): Performs multiple imputation and transformation of missing data.
- aregImpute(): Performs multiple imputation using additive regression.
- fit.mult.impute(): Fits regression models to multiply imputed datasets.
Datasets:
none
Further Information:
Package repository: https://github.com/harrelfe/Hmisc/
Documentation: https://www.rdocumentation.org/packages/Hmisc/versions/5.2-3/
Web-page: https://hbiostat.org/r/hmisc/
Input:
data.frame, tibble
Example:
You can find a nice vignette with example usage here