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These functions estimate the latent factors of a given matrix, no matter it is high-dimensional or not. It tries to first estimate the number of factors using bi-cross-validation and then estimate the latent factor matrix and the noise variances. For more information about the method, see Art B. Owen and Jingshu Wang 2015 archived article on factor model (<doi:10.48550/arXiv.1503.03515>).
| Version: | 1.2.1.1 |
| Depends: | R (≥ 3.0.2) |
| Imports: | corpcor, svd |
| Suggests: | MASS |
| Published: | 2022-06-30 |
| Author: | Art B. Owen [aut], Jingshu Wang [aut, cre] |
| Maintainer: | Jingshu Wang <wangjingshususan at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| In views: | Psychometrics |
| CRAN checks: | esaBcv results |
| Reference manual: | esaBcv.pdf |
| Package source: | esaBcv_1.2.1.1.tar.gz |
| Windows binaries: | r-devel: esaBcv_1.2.1.1.zip, r-release: esaBcv_1.2.1.1.zip, r-oldrel: esaBcv_1.2.1.1.zip |
| macOS binaries: | r-release (arm64): esaBcv_1.2.1.1.tgz, r-oldrel (arm64): esaBcv_1.2.1.1.tgz, r-release (x86_64): esaBcv_1.2.1.1.tgz, r-oldrel (x86_64): esaBcv_1.2.1.1.tgz |
| Old sources: | esaBcv archive |
| Reverse imports: | cate |
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