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 <arXiv:1802.02251> for detail.
| Version: | 1.0.2 |
| Depends: | R (≥ 3.0.2) |
| Imports: | mvtnorm, equSA, huge, ncvreg |
| Published: | 2020-02-19 |
| Author: | Bochao Jia [aut, ctb, cre, cph], Faming Liang [ctb] |
| Maintainer: | Bochao Jia <jbc409 at ufl.edu> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| CRAN checks: | IROmiss results |
| Reference manual: | IROmiss.pdf |
| Package source: | IROmiss_1.0.2.tar.gz |
| Windows binaries: | r-devel: IROmiss_1.0.2.zip, r-release: IROmiss_1.0.2.zip, r-oldrel: IROmiss_1.0.2.zip |
| macOS binaries: | r-release: IROmiss_1.0.2.tgz, r-oldrel: IROmiss_1.0.2.tgz |
| Old sources: | IROmiss archive |
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