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Enables the generation of Laplace factor models across diverse Laplace distributions and facilitates the application of Sparse Online Principal Component (SOPC), Incremental Principal Component (IPC), Parallel Principal Component (PPC), Sparse Approximate Principal Component (SAPC), Standard Principal Component (SPC), and Farm Test methods to these models. Evaluates the efficacy of these methods within the context of Laplace factor models by scrutinizing parameter estimation accuracy, mean square error, and the degree of sparsity.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | stats, FarmTest, MASS, SOPC, LaplacesDemon, matrixcalc, relliptical |
| Suggests: | testthat (≥ 3.0.0) |
| Published: | 2024-11-19 |
| DOI: | 10.32614/CRAN.package.LFM |
| Author: | Guangbao Guo [aut, cre], Siqi Liu [aut] |
| Maintainer: | Guangbao Guo <ggb11111111 at 163.com> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Language: | en-US |
| CRAN checks: | LFM results |
| Reference manual: | LFM.pdf |
| Package source: | LFM_0.1.0.tar.gz |
| Windows binaries: | r-devel: LFM_0.1.0.zip, r-release: not available, r-oldrel: LFM_0.1.0.zip |
| macOS binaries: | r-release (arm64): LFM_0.1.0.tgz, r-oldrel (arm64): LFM_0.1.0.tgz, r-release (x86_64): LFM_0.1.0.tgz, r-oldrel (x86_64): LFM_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.