Package: CKNNRLD 0.2.2
CKNNRLD: Clustering-Based K-Nearest Neighbor Regression for Longitudinal Data
Implements the 'CKNNRLD' algorithm (Clustering-Based K-Nearest Neighbor Regression for Longitudinal Data) for improving K-Nearest Neighbor ('KNN') regression on longitudinal data through cluster-based partitioning and localized prediction. Offers enhanced computational efficiency and accuracy for high-volume longitudinal datasets. The acronym 'KNN' stands for K-Nearest Neighbor. References: Loeloe MS, Tabatabaei SM, Sefidkar R, Mehrparvar AH, Jambarsang S (2025). "Boosting K-nearest neighbor regression performance for longitudinal data through a novel learning approach." BMC Bioinformatics, 26, 232. <doi:10.1186/s12859-025-06205-1>.
Authors:
CKNNRLD_0.2.2.tar.gz
CKNNRLD_0.2.2.zip(r-4.7-any)CKNNRLD_0.2.2.zip(r-4.6-any)CKNNRLD_0.2.2.zip(r-4.5-any)
CKNNRLD_0.2.2.tgz(r-4.6-any)CKNNRLD_0.2.2.tgz(r-4.5-any)
CKNNRLD_0.2.2.tar.gz(r-4.7-any)CKNNRLD_0.2.2.tar.gz(r-4.6-any)
CKNNRLD_0.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
CKNNRLD/json (API)
| # Install 'CKNNRLD' in R: |
| install.packages('CKNNRLD', repos = c('https://mslbiostat.r-universe.dev', 'https://cloud.r-project.org')) |
- CKNNRLD_example - Simulated longitudinal data for CKNNRLD example
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:1815ed7afc. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 239 | ||
| source / vignettes | OK | 278 | ||
| linux-release-x86_64 | OK | 220 | ||
| macos-release-arm64 | OK | 104 | ||
| macos-oldrel-arm64 | OK | 149 | ||
| windows-devel | OK | 158 | ||
| windows-release | OK | 137 | ||
| windows-oldrel | OK | 122 | ||
| wasm-release | OK | 204 |
Exports:benchmark.CKNNRLDBestCCKNNRLDCKNNRLD.tuneimportance.CKNNRLDKNNRLDKNNRLD.tuneperformance.CKNNRLDsimulateCKNNRLD
Dependencies:askpassbase64encBHbigassertrbigparallelrbigstatsrbitbslibcachemclassclassIntclicodetoolscowplotcpp11curlDBIdigestDirectionaldoParallele1071evaluatefarverfastmapffflockfontawesomeforeachfsggplot2gluegtablehighrhtmltoolshtmlwidgetshttrisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemimeopensslparallellypillarpkgconfigproxypsR6rangenrappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRfastRfast2rglRhpcBLASctlrlangrmarkdownrmioRnanoflannrnaturalearthRSpectras2S7sassscalessfsysterratibbletinytexunitsutf8vctrsviridisLitewithrwkxfunyamlzigg
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Benchmark CKNNRLD vs KNNRLD | benchmark.CKNNRLD |
| Find Optimal Number of Clusters for Longitudinal Data | BestC |
| Clustering-based KNN Regression for Longitudinal Data (CKNNRLD) | CKNNRLD |
| Simulated longitudinal data for CKNNRLD example | CKNNRLD_example |
| Tune CKNNRLD Model with Automatic Cluster Selection | CKNNRLD.tune |
| Extract model information from CKNNRLD | coef.CKNNRLD |
| Extract fitted values from CKNNRLD model | fitted.CKNNRLD |
| Variable Importance for CKNNRLD using Permutation | importance.CKNNRLD |
| Standard K-Nearest Neighbor Regression for Longitudinal Data | KNNRLD |
| Tune k in KNNRLD using Cross-Validation | KNNRLD.tune |
| Performance metrics for CKNNRLD models | performance.CKNNRLD |
| Plot method for CKNNRLD objects | plot.CKNNRLD |
| Predict method for CKNNRLD objects | predict.CKNNRLD |
| Print method for CKNNRLD objects | print.CKNNRLD |
| Extract residuals from CKNNRLD model | residuals.CKNNRLD |
| Simulate Longitudinal Data for CKNNRLD | simulateCKNNRLD |
| Summary method for CKNNRLD objects | summary.CKNNRLD |
