Distributed Trimmed Scores Regression for Handling Missing Data

Documentation for package ‘DTSR’ version 0.1.0

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
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.
mean	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