Package: SFM 0.1.0
SFM: A Package for Analyzing Skew Factor Models
Generates Skew Factor Models data and applies Sparse Online Principal Component (SOPC) method to estimate model parameters. It includes capabilities for calculating mean squared error, relative error, and sparsity of the loading matrix. Additionally, it includes robust regression methods such as adaptive Huber regression.The philosophy of the package is described in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.
Authors:
SFM_0.1.0.tar.gz
SFM_0.1.0.zip(r-4.5)SFM_0.1.0.zip(r-4.4)SFM_0.1.0.zip(r-4.3)
SFM_0.1.0.tgz(r-4.4-any)SFM_0.1.0.tgz(r-4.3-any)
SFM_0.1.0.tar.gz(r-4.5-noble)SFM_0.1.0.tar.gz(r-4.4-noble)
SFM_0.1.0.tgz(r-4.4-emscripten)SFM_0.1.0.tgz(r-4.3-emscripten)
SFM.pdf |SFM.html✨
SFM/json (API)
| # Install 'SFM' in R: |
| install.packages('SFM', repos = c('https://guangbaog.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 11 days agofrom:07e4b16b50. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 18 2024 |
| R-4.5-win | OK | Nov 18 2024 |
| R-4.5-linux | OK | Nov 18 2024 |
| R-4.4-win | OK | Nov 18 2024 |
| R-4.4-mac | OK | Nov 18 2024 |
| R-4.3-win | OK | Nov 18 2024 |
| R-4.3-mac | OK | Nov 18 2024 |
Exports:calculate_errorshuber.reg.adaptive.skewSFMSOPC_estimation
Dependencies:elasticnetlarslatticemagrittrMASSMatrixmatrixcalcMatrixModelsmnormtnumDerivquantregsnSOPCSparseMsurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| calculate_errors Function | calculate_errors |
| Adaptive Huber Regression for Skew Factor Models | huber.reg.adaptive.skew |
| The SFM function is to generate Skew Factor Models data. | SFM |
| SOPC Estimation Function | SOPC_estimation |
