Package: ASML 1.0.0
ASML: Algorithm Portfolio Selection with Machine Learning
A wrapper for machine learning (ML) methods to select among a portfolio of algorithms based on the value of a key performance indicator (KPI). A number of features is used to adjust a model to predict the value of the KPI for each algorithm, then, for a new value of the features the KPI is estimated and the algorithm with the best one is chosen. To learn it can use the regression methods in 'caret' package or a custom function defined by the user. Several graphics available to analyze the results obtained. This library has been used in Ghaddar et al. (2023) <doi:10.1287/ijoc.2022.0090>).
Authors:
ASML_1.0.0.tar.gz
ASML_1.0.0.zip(r-4.5)ASML_1.0.0.zip(r-4.4)ASML_1.0.0.zip(r-4.3)
ASML_1.0.0.tgz(r-4.5-any)ASML_1.0.0.tgz(r-4.4-any)ASML_1.0.0.tgz(r-4.3-any)
ASML_1.0.0.tar.gz(r-4.5-noble)ASML_1.0.0.tar.gz(r-4.4-noble)
ASML_1.0.0.tgz(r-4.4-emscripten)ASML_1.0.0.tgz(r-4.3-emscripten)
ASML.pdf |ASML.html✨
ASML/json (API)
| # Install 'ASML' in R: |
| install.packages('ASML', repos = c('https://brais-gonzalez.r-universe.dev', 'https://cloud.r-project.org')) |
- branching - Branching point selection in Polynomial Optimization
- branchingsmall - Branching point selection in Polynomial Optimization
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 25 days agofrom:443982853f. Checks:8 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Feb 20 2025 |
| R-4.5-win | OK | Feb 20 2025 |
| R-4.5-mac | OK | Feb 20 2025 |
| R-4.5-linux | OK | Feb 20 2025 |
| R-4.4-win | OK | Feb 20 2025 |
| R-4.4-mac | OK | Feb 20 2025 |
| R-4.3-win | OK | Feb 20 2025 |
| R-4.3-mac | OK | Feb 20 2025 |
Exports:ASpredictAStrainboxplotsfigure_comparisonKPI_summary_tableKPI_tablemlpartition_and_normalizeranking
Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrPolychromepROCprodlimprogressrproxypurrrR6RColorBrewerRcpprecipesreshape2rlangrpartscalesscatterplot3dshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Predicting the KPI value for the algorithms | ASpredict |
| Predicting the KPI value for the algorithms | ASpredict.as_train |
| Training models for posterior selection of algorithms | AStrain |
| Training models for posterior selection of algorithms | AStrain.as_data |
| Boxplots | boxplots |
| Boxplots | boxplots.as_data |
| Branching point selection in Polynomial Optimization | branching |
| Branching point selection in Polynomial Optimization | branchingsmall |
| Figure comparison | figure_comparison |
| Figure Comparison | figure_comparison.as_data |
| KPI summary table | KPI_summary_table KPI_summary_table.as_data |
| KPI table | KPI_table KPI_table.as_data |
| Machine learning process | ml |
| Partition and Normalize | partition_and_normalize |
| Plot | plot.as_data |
| Ranking | ranking |
| Ranking Plot | ranking.as_data |
