Package: ldbod 0.1.2
ldbod: Local Density-Based Outlier Detection
Flexible procedures to compute local density-based outlier scores for ranking outliers. Both exact and approximate nearest neighbor search can be implemented, while also accommodating multiple neighborhood sizes and four different local density-based methods. It allows for referencing a random subsample of the input data or a user specified reference data set to compute outlier scores against, so both unsupervised and semi-supervised outlier detection can be implemented.
Authors:
ldbod_0.1.2.tar.gz
ldbod_0.1.2.zip(r-4.5)ldbod_0.1.2.zip(r-4.4)ldbod_0.1.2.zip(r-4.3)
ldbod_0.1.2.tgz(r-4.4-any)ldbod_0.1.2.tgz(r-4.3-any)
ldbod_0.1.2.tar.gz(r-4.5-noble)ldbod_0.1.2.tar.gz(r-4.4-noble)
ldbod_0.1.2.tgz(r-4.4-emscripten)ldbod_0.1.2.tgz(r-4.3-emscripten)
ldbod.pdf |ldbod.html✨
ldbod/json (API)
| # Install 'ldbod' in R: |
| install.packages('ldbod', repos = c('https://kwilliams83.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/kwilliams83/ldbod/issues
Last updated 8 years agofrom:a93a70c7db. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 10 2024 |
| R-4.5-win | OK | Nov 10 2024 |
| R-4.5-linux | OK | Nov 10 2024 |
| R-4.4-win | OK | Nov 10 2024 |
| R-4.4-mac | OK | Nov 10 2024 |
| R-4.3-win | OK | Nov 10 2024 |
| R-4.3-mac | OK | Nov 10 2024 |
