Package: DTSR 0.2.2

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]

DTSR_0.2.2.tar.gz
DTSR_0.2.2.zip(r-4.7-any)DTSR_0.2.2.zip(r-4.6-any)DTSR_0.2.2.zip(r-4.5-any)
DTSR_0.2.2.tgz(r-4.6-any)DTSR_0.2.2.tgz(r-4.5-any)
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DTSR_0.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
DTSR/json (API)

# Install 'DTSR' in R:

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

Datasets:

abalone - Abalone Data
Frogs - Frogs Data

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 410 downloads 13 exports 89 dependencies

Last updated from:01e0f939a4. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	164
source / vignettes	OK	434
linux-release	OK	176
macos-release	OK	140
macos-oldrel	OK	104
windows-devel	OK	99
windows-release	OK	107
windows-oldrel	OK	128
wasm-release	OK	138

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

Dependencies:abind backports boot broom car carData class cli cluster colorspace cowplot cpp11 data.table DEoptimR Deriv doBy dplyr e1071 farver forecast Formula fracdiff generics ggplot2 glue gtable isoband jsonlite labeling laeken lattice lifecycle lme4 lmtest magrittr MASS Matrix MatrixModels mgcv minqa mnormt modelr moments mvdalab nlme nloptr nnet numDeriv pbkrtest penalized pillar pkgconfig plyr proxy purrr quantreg R6 ranger rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack reformulas reshape2 rlang robustbase S7 scales sn sp SparseM stringi stringr survival tibble tidyr tidyselect timeDate urca utf8 vcd vctrs VIM viridisLite withr xgboost zoo

Help page	Topics
Abalone Data	abalone
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
Frogs Data	Frogs
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	meanImpute
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 0.2.2

DTSR: Distributed Trimmed Scores Regression for Handling Missing Data

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Help Manual

Usage by other packages (reverse dependencies)