R: Distributed Trimmed Scores Regression for Handling Missing Data

# Distributed Trimmed Scores Regression for Handling Missing Data

## Documentation for package ‘DTSR’ version 0.2.2

- DESCRIPTION file

## Help Pages

abalone | Abalone Data
DEM | Distributed EM Imputation (DEM) for Handling Missing Data
DRPCA | Distributed Robust Principal Component Analysis (DRPCA) for Handling Missing Data
DTSR | Distributed Trimmed Scores Regression (DTSR) for Handling Missing Data
EM | Expectation-Maximization Imputation with Evaluation Metrics
Frogs | Frogs Data
IndexCPP | Calculate the Consistency Proportion Index (CPP)
KNN | This function performs imputation using the K-Nearest Neighbors (KNN) algorithm and calculates various evaluation metrics including RMSE, MMAE, RRE, and Consistency Proportion Index (CPP) using different hierarchical clustering methods. It also records the execution time of the process.
meanImpute | Mean Imputation with Evaluation Metrics
MLPCA | Multilinear Principal Component Analysis with Missing Data
NIPALS | NIPALS Algorithm with RPCA and Clustering
RPCA | Robust Principal Component Analysis with Missing Data
SVD | This function performs imputation using Singular Value Decomposition (SVD) and calculates various evaluation metrics including RMSE, MMAE, RRE, and Consistency Proportion Index (CPP) using different hierarchical clustering methods.
SVDImpute | Improved SVD Imputation
TSR | Trimmed Scores Regression with Missing Data