Package: DTSR 0.1.0

DTSR: Distributed Trimmed Scores Regression for Handling Missing Data

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) <doi:10.1080/03610918.2022.2091779>.

Authors:Guangbao Guo [aut, cre, cph], Ruiling Niu [aut]

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DTSR.pdf |DTSR.html✨
DTSR/json (API)

# Install 'DTSR' in R:

install.packages('DTSR', repos = c('https://guangbaog.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 13 exports 89 dependencies

Last updated 15 days agofrom:b18b86c4e3. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 09 2024
R-4.5-win	OK	Nov 09 2024
R-4.5-linux	OK	Nov 09 2024
R-4.4-win	OK	Nov 09 2024
R-4.4-mac	OK	Nov 09 2024
R-4.3-win	OK	Nov 09 2024
R-4.3-mac	OK	Nov 09 2024

Exports:DEM DRPCA DTSR EM IndexCPP KNN mean MLPCA NIPALS RPCA SVD SVDImpute TSR

Dependencies:abind backports bit bit64 boot broom car carData class cli clipr cluster colorspace cowplot cpp11 crayon curl DBI Deriv DMwR2 doBy dplyr fansi farver Formula generics ggplot2 glue gtable hms isoband jsonlite labeling lattice lifecycle lme4 magrittr MASS Matrix MatrixModels mgcv microbenchmark minqa mnormt modelr moments munsell mvdalab nlme nloptr nnet numDeriv pbkrtest penalized pillar pkgconfig plyr prettyunits progress purrr quantmod quantreg R6 RColorBrewer Rcpp RcppArmadillo RcppEigen readr reshape2 rlang rpart scales sn SparseM stringi stringr survival tibble tidyr tidyselect TTR tzdb utf8 vctrs viridisLite vroom withr xts zoo

Help page	Topics
Distributed EM Imputation (DEM) for Handling Missing Data	DEM
Distributed Robust Principal Component Analysis (DRPCA) for Handling Missing Data	DRPCA
Distributed Trimmed Scores Regression (DTSR) for Handling Missing Data	DTSR
Expectation-Maximization Imputation with Evaluation Metrics	EM
Calculate the Consistency Proportion Index (CPP)	IndexCPP
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.	KNN
Mean Imputation with Evaluation Metrics	mean
Multilinear Principal Component Analysis with Missing Data	MLPCA
NIPALS Algorithm with RPCA and Clustering	NIPALS
Robust Principal Component Analysis with Missing Data	RPCA
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.	SVD
Improved SVD Imputation	SVDImpute
Trimmed Scores Regression with Missing Data	TSR

Package: DTSR 0.1.0

DTSR: Distributed Trimmed Scores Regression for Handling Missing Data

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