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DTSR (version 0.2.0)

Distributed Trimmed Scores Regression for Handling Missing Data

Description

Provides functions for handling missing data using Distributed Trimmed Scores Regression and other imputation methods. It includes facilities for data imputation, evaluation metrics, and clustering analysis. It is designed to work in distributed computing environments to handle large datasets efficiently. The philosophy of the package is described in Guo G. (2024) .

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Version

Install

install.packages('DTSR')

Monthly Downloads

1

Version

0.2.0

License

GPL-3

Maintainer

Guangbao Guo

Last Published

April 27th, 2025

Functions in DTSR (0.2.0)

mean

Mean Imputation with Evaluation Metrics
RPCA

Robust Principal Component Analysis with Missing Data
NIPALS

NIPALS Algorithm with RPCA and Clustering
EM

Expectation-Maximization Imputation with Evaluation Metrics
DRPCA

Distributed Robust Principal Component Analysis (DRPCA) for Handling Missing Data
DEM

Distributed EM Imputation (DEM) for Handling Missing Data
DTSR

Distributed Trimmed Scores Regression (DTSR) for Handling Missing Data
Frogs

Frogs Data
abalone

Abalone Data
TSR

Trimmed Scores Regression with Missing Data
IndexCPP

Calculate the Consistency Proportion Index (CPP)
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
MLPCA

Multilinear Principal Component Analysis with Missing Data
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.