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.

Version: 0.0.6
Depends: svMisc
Suggests: NMF, recommenderlab
Published: 2019-07-02
Author: Nick Kunz
Maintainer: Nick Kunz <nick.kunz at columbia.edu>
License: GPL-3
NeedsCompilation: no
Materials: NEWS
CRAN checks: crassmat results

Downloads:

Reference manual: crassmat.pdf
Package source: crassmat_0.0.6.tar.gz
Windows binaries: r-devel: crassmat_0.0.6.zip, r-release: crassmat_0.0.6.zip, r-oldrel: crassmat_0.0.6.zip
OS X binaries: r-release: crassmat_0.0.6.tgz, r-oldrel: crassmat_0.0.6.tgz

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