Package: learner 0.2.0
learner: Latent Space-Based Transfer Learning
Implements transfer learning methods for low-rank matrix estimation. These methods leverage similarity in the latent row and column spaces between the source and target populations to improve estimation in the target population. The methods include the LatEnt spAce-based tRaNsfer lEaRning (LEARNER) method and the direct projection LEARNER (D-LEARNER) method described by McGrath et al. (2024) <doi:10.48550/arXiv.2412.20605>.
Authors:
learner_0.2.0.tar.gz
learner_0.2.0.zip(r-4.5)learner_0.2.0.zip(r-4.4)learner_0.2.0.zip(r-4.3)
learner_0.2.0.tgz(r-4.4-any)learner_0.2.0.tgz(r-4.3-any)
learner_0.2.0.tar.gz(r-4.5-noble)learner_0.2.0.tar.gz(r-4.4-noble)
learner_0.2.0.tgz(r-4.4-emscripten)learner_0.2.0.tgz(r-4.3-emscripten)
learner.pdf |learner.html✨
learner/json (API)
NEWS
| # Install 'learner' in R: |
| install.packages('learner', repos = c('https://stmcg.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/stmcg/learner/issues
- dat_highsim - Simulated data set: High similarity in the latent spaces
- dat_modsim - Simulated data set: Moderate similarity in the latent spaces
Last updated 1 days agofrom:fb6e19f918. Checks:7 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 12 2025 |
| R-4.5-win | OK | Jan 12 2025 |
| R-4.5-linux | OK | Jan 12 2025 |
| R-4.4-win | OK | Jan 12 2025 |
| R-4.4-mac | OK | Jan 12 2025 |
| R-4.3-win | OK | Jan 12 2025 |
| R-4.3-mac | OK | Jan 12 2025 |
Exports:cv.learnerdlearnerlearner
Dependencies:codetoolsdoParallelforeachiteratorsScreeNOT
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Cross-validation for LEARNER | cv.learner |
| Simulated data set: High similarity in the latent spaces | dat_highsim |
| Simulated data set: Moderate similarity in the latent spaces | dat_modsim |
| Latent space-based transfer learning | dlearner |
| Latent space-based transfer learning | learner |
