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RMCLab  

Lab for Matrix Completion and Imputation of Discrete Rating Data
View on CRAN: Click here


Download and install RMCLab package within the R console
Install from CRAN:
install.packages("RMCLab")

Install from Github:
library("remotes")
install_github("cran/RMCLab")

Install by package version:
library("remotes")
install_version("RMCLab", "0.1.0")



Attach the package and use:
library("RMCLab")
Maintained by
Andreas Alfons
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-07-28
Latest Update: 2025-07-28
Description:
Collection of methods for rating matrix completion, which is a statistical framework for recommender systems. Another relevant application is the imputation of rating-scale survey data in the social and behavioral sciences. Note that matrix completion and imputation are synonymous terms used in different streams of the literature. The main functionality implements robust matrix completion for discrete rating-scale data with a low-rank constraint on a latent continuous matrix (Archimbaud, Alfons, and Wilms (2025) <doi:10.48550/arXiv.2412.20802>). In addition, the package provides wrapper functions for 'softImpute' (Mazumder, Hastie, and Tibshirani, 2010, <https://www.jmlr.org/papers/v11/mazumder10a.html>; Hastie, Mazumder, Lee, Zadeh, 2015, <https://www.jmlr.org/papers/v16/hastie15a.html>) for easy tuning of the regularization parameter, as well as benchmark methods such as median imputation and mode imputation.
How to cite:
Andreas Alfons (2025). RMCLab: Lab for Matrix Completion and Imputation of Discrete Rating Data. R package version 0.1.0, https://cran.r-project.org/web/packages/RMCLab. Accessed 29 Oct. 2026.
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Complete documentation for RMCLab
Functions, R codes and Examples using the RMCLab R package
Full RMCLab package functions and examples
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