Package:
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.
Algorithms for Imputation:
impNorm(): Draws missing elements of a data matrix under the multivariate normal model and a user-supplied parameter,impSeq(): Impute missing multivariate data using sequential algorithm,impSeqRob(): Impute missing multivariate data using robust sequential algorithm.
Other Algorithms:
PcaNA()- Computes classical and robust principal components for incomplete data using an EM algorithm as descibed by Serneels and Verdonck (2008)
Datasets:
bush10- Campbell Bushfire Data with added missing data items(10 percent),ces- Consumer Expenditure Survey Data.
Further Information:
Package repository: https://github.com/valentint/rrcovNA
Documentation: https://www.rdocumentation.org/packages/rrcovNA/versions/0.5-2
Valentin Todorov, Matthias Templ, Detection of multivariate outliers in business survey data with incomplete information, DOI:10.1007/s11634-010-0075-2
Input:
data.frame
Example:
You can find a nice vignette with example usage here