IROmiss: Imputation Regularized Optimization Algorithm

Missing data are frequently encountered in high-dimensional data analysis, but they are usually difficult to deal with using standard algorithms, such as the EM algorithm and its variants. This package provides a general algorithm, the so-called Imputation Regularized Optimization (IRO) algorithm, for high-dimensional missing data problems. You can refer to Liang, F., Jia, B., Xue, J., Li, Q. and Luo, Y. (2018) at <https://people.clas.ufl.edu/yeluo/files/ica10.pdf> for detail. The publication "An Imputation Regularized Optimization Algorithm for High-Dimensional Missing Data Problems and Beyond" will be appear on Journal of the Royal Statistical Society Series B soon.

Version: 1.0.0
Depends: R (≥ 3.0.2)
Imports: mvtnorm, equSA, huge, ncvreg
Published: 2018-01-17
Author: Bochao Jia [aut, cre, cph], Faming Liang [ctb]
Maintainer: Bochao Jia <jbc409 at ufl.edu>
License: GPL-2
NeedsCompilation: yes
CRAN checks: IROmiss results

Downloads:

Reference manual: IROmiss.pdf
Package source: IROmiss_1.0.0.tar.gz
Windows binaries: r-devel: IROmiss_1.0.0.zip, r-release: IROmiss_1.0.0.zip, r-oldrel: IROmiss_1.0.0.zip
OS X El Capitan binaries: r-release: IROmiss_1.0.0.tgz
OS X Mavericks binaries: r-oldrel: not available

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