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.
Read more →Package:
imputeLCMD
Authors:
Samuel Wieczorek, Thomas Burger, Cosmin Lazar
Category:
Left-Censored Missing Data
Use-Cases:
Left-censored data imputation, gene expression data generation
Popularity:
Description:
A collection of functions for left-censored missing data imputation. Left-censoring is a special case of missing not at random (MNAR) mechanism that generates non-responses in proteomics experiments. The package also contains functions to artificially generate peptide/protein expression data (log-transformed) as random draws from a multivariate Gaussian distribution as well as a function to generate missing data (both randomly and non-randomly). For comparison reasons, the package also contains several wrapper functions for the imputation of non-responses that are missing at random. * New functionality has been added: a hybrid method that allows the imputation of missing values in a more complex scenario where the missing data are both MAR and MNAR.
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