Package: HMTL 0.1.0
HMTL: Heterogeneous Multi-Task Feature Learning
The heterogeneous multi-task feature learning is a data integration method to conduct joint feature selection across multiple related data sets with different distributions. The algorithm can combine different types of learning tasks, including linear regression, Huber regression, adaptive Huber, and logistic regression. The modified version of Bayesian Information Criterion (BIC) is produced to measure the model performance. Package is based on Yuan Zhong, Wei Xu, and Xin Gao (2022) <https://www.fields.utoronto.ca/talk-media/1/53/65/slides.pdf>.
Authors:
HMTL_0.1.0.tar.gz
HMTL_0.1.0.zip(r-4.5)HMTL_0.1.0.zip(r-4.4)HMTL_0.1.0.zip(r-4.3)
HMTL_0.1.0.tgz(r-4.4-any)HMTL_0.1.0.tgz(r-4.3-any)
HMTL_0.1.0.tar.gz(r-4.5-noble)HMTL_0.1.0.tar.gz(r-4.4-noble)
HMTL_0.1.0.tgz(r-4.4-emscripten)HMTL_0.1.0.tgz(r-4.3-emscripten)
HMTL.pdf |HMTL.html✨
HMTL/json (API)
| # Install 'HMTL' in R: |
| install.packages('HMTL', repos = c('https://hermitz9.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 2 years agofrom:f00b438e84. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 26 2024 |
| R-4.5-win | OK | Oct 26 2024 |
| R-4.5-linux | OK | Oct 26 2024 |
| R-4.4-win | OK | Oct 26 2024 |
| R-4.4-mac | OK | Oct 26 2024 |
| R-4.3-win | OK | Oct 26 2024 |
| R-4.3-mac | OK | Oct 26 2024 |
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Heterogeneous Multi-task Feature Learning | HMTL |
| Mock Gene Data | mockdata1 mockdata2 |
| Multiple Classification Task Feature Learning | MTL_class |
| Heterogeneous Multi-task Feature Learning | MTL_hetero |
| Robust Multi-Task Feature Learning | MTL_reg |
| Plot diagram of the information criterion vs. penalty parameters | plot_HMTL |
| Model Selection for Multi-task Feature Learning based on Bayesian Information Criterion (BIC) | Selection_HMTL |
