nickkunz/crassmat: Conditional Random Sampling Sparse Matrices

Conducts conditional random sampling on observed values in sparse matrices. Useful for training and test set splitting sparse matrices prior to model fitting in cross-validation procedures and estimating the predictive accuracy of data imputation methods, such as matrix factorization or singular value decomposition (SVD). Although designed for applications with sparse matrices, CRASSMAT can also be applied to complete matrices, as well as to those containing missing values.

Getting started

Package details

AuthorNick Kunz
MaintainerNick Kunz <[email protected]>
LicenseGPL-3
Version0.0.6
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("nickkunz/crassmat")
nickkunz/crassmat documentation built on Dec. 27, 2019, 4:31 a.m.