Package: learner 1.0.0

Sean McGrath

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:Sean McGrath [aut, cre], Ryan ODea [aut], Cenhao Zhu [aut], Rui Duan [aut]

learner_1.0.0.tar.gz
learner_1.0.0.zip(r-4.7-x86_64)learner_1.0.0.zip(r-4.7-arm64)learner_1.0.0.zip(r-4.6-x86_64)learner_1.0.0.zip(r-4.6-arm64)
learner_1.0.0.tar.gz(r-4.7-arm64)learner_1.0.0.tar.gz(r-4.7-x86_64)learner_1.0.0.tar.gz(r-4.6-arm64)learner_1.0.0.tar.gz(r-4.6-x86_64)
learner_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
learner/json (API)

# Install 'learner' in R:
install.packages('learner', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/stmcg/learner/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • dat_highsim - Simulated data set: High similarity in the latent spaces
  • dat_modsim - Simulated data set: Moderate similarity in the latent spaces

On CRAN:

Conda:

cppopenmp

1.70 score 4 scripts 278 downloads 3 exports 3 dependencies

Last updated from:1b0c4f9105. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK140
linux-devel-x86_64OK174
source / vignettesOK212
linux-release-arm64OK143
linux-release-x86_64OK135
windows-devel-arm64OK210
windows-devel-x86_64OK152
windows-release-arm64OK269
windows-release-x86_64OK117
wasm-releaseOK108

Exports:cv.learnerdlearnerlearner

Dependencies:RcppRcppEigenScreeNOT