Package: tensorsparse 3.0
tensorsparse: Multiway Clustering via Tensor Block Models
Implements the multiway sparse clustering approach of M. Wang and Y. Zeng, "Multiway clustering via tensor block models". Advances in Neural Information Processing System 32 (NeurIPS), 715-725, 2019.
Authors:
tensorsparse_3.0.tar.gz
tensorsparse_3.0.zip(r-4.5)tensorsparse_3.0.zip(r-4.4)tensorsparse_3.0.zip(r-4.3)
tensorsparse_3.0.tgz(r-4.5-x86_64)tensorsparse_3.0.tgz(r-4.5-arm64)tensorsparse_3.0.tgz(r-4.4-x86_64)tensorsparse_3.0.tgz(r-4.4-arm64)tensorsparse_3.0.tgz(r-4.3-x86_64)tensorsparse_3.0.tgz(r-4.3-arm64)
tensorsparse_3.0.tar.gz(r-4.5-noble)tensorsparse_3.0.tar.gz(r-4.4-noble)
tensorsparse_3.0.tgz(r-4.4-emscripten)tensorsparse_3.0.tgz(r-4.3-emscripten)
tensorsparse.pdf |tensorsparse.html✨
tensorsparse/json (API)
NEWS
| # Install 'tensorsparse' in R: |
| install.packages('tensorsparse', repos = c('https://yzeng58.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 4 years agofrom:34d0ad9eec. Checks:12 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 06 2025 |
| R-4.5-win-x86_64 | OK | Mar 06 2025 |
| R-4.5-mac-x86_64 | OK | Mar 06 2025 |
| R-4.5-mac-aarch64 | OK | Mar 06 2025 |
| R-4.5-linux-x86_64 | OK | Mar 06 2025 |
| R-4.4-win-x86_64 | OK | Mar 06 2025 |
| R-4.4-mac-x86_64 | OK | Mar 06 2025 |
| R-4.4-mac-aarch64 | OK | Mar 06 2025 |
| R-4.4-linux-x86_64 | OK | Mar 06 2025 |
| R-4.3-win-x86_64 | OK | Mar 06 2025 |
| R-4.3-mac-x86_64 | OK | Mar 06 2025 |
| R-4.3-mac-aarch64 | OK | Mar 06 2025 |
Exports:chooseClusteringSizechooseLambdagetOrder3TensorgetOrder4TensortbmClustering
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Select the clustering size for order-3 sparse tensor clustering via BIC | chooseClusteringSize |
| Select the regularization coefficient for order-3 sparse tensor clustering via BIC | chooseLambda |
| Generate a random order-3 tensor | getOrder3Tensor |
| Generate a random order-4 tensor | getOrder4Tensor |
| Perform tensor clustering via tensor block model (TBM) | tbmClustering |
