Package: DTSR 0.2.2

Guangbao Guo

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

# Install 'DTSR' in R:

install.packages('DTSR', repos = c('https://cran.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 13 downloads 13 exports 116 dependencies

Last updated from:01e0f939a4. Checks:4 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	172
source / vignettes	OK	230
linux-release-x86_64	OK	181
wasm-release	OK	126

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

Dependencies:abind backports bbotk boot broom car carData checkmate class cli cluster codetools colorspace cowplot cpp11 data.table DEoptimR Deriv digest doBy dplyr e1071 evaluate farver forecast Formula fracdiff future future.apply generics ggplot2 globals glue gtable isoband jsonlite labeling laeken lattice lgr lifecycle listenv lme4 lmtest magrittr MASS Matrix MatrixModels matrixStats mgcv microbenchmark minqa mirai mlbench mlr3 mlr3learners mlr3measures mlr3misc mlr3pipelines mlr3tuning mnormt modelr moments moocore mvdalab nanonext nlme nloptr nnet numDeriv palmerpenguins paradox parallelly pbkrtest penalized pillar pkgconfig plyr proxy PRROC 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 uuid 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

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)