Paper

.github/workflows/draft-pdf.yml

@@ -0,0 +1,28 @@
+name: Draft PDF
+on:
+  push:
+    paths:
+      - paper/**
+      - .github/workflows/draft-pdf.yml
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: paper/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v4
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: paper/paper.pdf

R/learner_abess_classif_abess.R

@@ -1,5 +1,5 @@
 #' @title Classification Abess Learner
-#' @author abess-team
+#' @author bbayukari
 #' @name mlr_learners_classif.abess
 #'
 #' @description

R/learner_abess_regr_abess.R

@@ -1,5 +1,5 @@
 #' @title Regression Abess Learner
-#' @author abess-team
+#' @author bbayukari
 #' @name mlr_learners_regr.abess
 #'
 #' @description

paper/paper.bib

@@ -0,0 +1,291 @@
+@book{Bischl2024,
+    title = {Applied Machine Learning Using {m}lr3 in {R}},
+    editor = {Bernd Bischl and Raphael Sonabend and Lars Kotthoff and Michel Lang},
+    url = {https://mlr3book.mlr-org.com},
+    year = {2024},
+    isbn = {9781032507545},
+    publisher = {CRC Press}
+}
+
+@article{Lang2019,
+    author = {Lang, Michel and Binder, Martin and Richter, Jakob and Schratz, Patrick and Pfisterer, Florian and Coors, Stefan and Au, Quay and Casalicchio, Giuseppe and Kotthoff, Lars and Bischl, Bernd},
+    doi = {10.21105/JOSS.01903},
+    issn = {2475-9066},
+    journal = {Journal of Open Source Software},
+    month = {dec},
+    number = {44},
+    pages = {1903},
+    publisher = {The Open Journal},
+    title = {{mlr3: A modern object-oriented machine learning framework in R}},
+    url = {https://joss.theoj.org/papers/10.21105/joss.01903},
+    volume = {4},
+    year = {2019}
+}
+
+@Manual{R,
+    title = {R: A Language and Environment for Statistical Computing},
+    author = {{R Core Team}},
+    organization = {R Foundation for Statistical Computing},
+    address = {Vienna, Austria},
+    year = {2019},
+    url = {https://www.R-project.org/},
+}
+
+@article{mlr,
+    author  = {Bernd Bischl and Michel Lang and Lars Kotthoff and Julia Schiffner and Jakob Richter and Erich Studerus and Giuseppe Casalicchio and Zachary M. Jones},
+    title   = {mlr: Machine Learning in R},
+    journal = {Journal of Machine Learning Research},
+    year    = {2016},
+    volume  = {17},
+    number  = {170},
+    pages   = {1-5},
+    url     = {http://jmlr.org/papers/v17/15-066.html}
+}
+
+@Manual{data.table,
+    title = {data.table: Extension of `data.frame`},
+    author = {Matt Dowle and Arun Srinivasan},
+    year = {2019},
+    note = {R package version 1.12.6},
+    url = {https://CRAN.R-project.org/package=data.table},
+}
+
+@article{sk-learn,
+    title={Scikit-learn: Machine Learning in {P}ython},
+    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
+        and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
+            and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
+            Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
+    journal={Journal of Machine Learning Research},
+    volume={12},
+    pages={2825--2830},
+    year={2011},
+    url = {http://jmlr.org/papers/v12/pedregosa11a.html}
+}
+
+@article{caret,
+   author = {Max Kuhn},
+   title = {Building Predictive Models in R Using the caret Package},
+   journal = {Journal of Statistical Software, Articles},
+   volume = {28},
+   number = {5},
+   year = {2008},
+   keywords = {},
+   pages = {1--26},
+   doi = {10.18637/jss.v028.i05},
+   url = {https://www.jstatsoft.org/v028/i05}
+}
+
+@article{weka,
+  title       = {{The WEKA Data Mining Software: An Update}},
+  author      = {Hall, M. and Frank, E. and Holmes, G. and Pfahringer, B. and Reutemann, P. and Witten, I. H. },
+  journal     = {ACM SIGKDD explorations newsletter},
+  volume      = {11},
+  number      = {1},
+  pages       = {10--18},
+  year        = {2009},
+  publisher   = {ACM},
+  doi         = {10.1145/1656274.1656278}
+}
+
+@Manual{tidymodels,
+  title = {Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles.},
+  author = {Max Kuhn and Hadley Wickham},
+  url = {https://www.tidymodels.org},
+  year = {2020}
+}
+
+@Manual{parsnip,
+  title = {parsnip: A Common API to Modeling and Analysis Functions},
+  author = {Max Kuhn and Davis Vaughan},
+  year = {2024},
+  note = {R package version 1.2.1, https://parsnip.tidymodels.org/},
+  url = {https://github.com/tidymodels/parsnip}
+}
+
+@Manual{mlr3tuning,
+  title = {mlr3tuning: Hyperparameter Optimization for 'mlr3'},
+  author = {Becker, Marc and Lang, Michel and Richter, Jakob and Bischl, Bernd and Schalk, Daniel},
+  year = {2024},
+  note = {R package version 1.2.1, https://mlr3tuning.mlr-org.com/},
+  url = {https://github.com/mlr-org/mlr3tuning}
+}
+
+@article{caret,
+  title={Building predictive models in R using the caret package},
+  author={Kuhn, Max},
+  journal={Journal of statistical software},
+  volume={28},
+  pages={1--26},
+  year={2008}
+}
+
+@software{mlj,
+  author       = {Anthony Blaom and Franz Kiraly and Thibaut Lienart and Sebastian Vollmer},
+  title        = {alan-turing-institute/MLJ.jl: v0.5.3},
+  month        = nov,
+  year         = 2019,
+  publisher    = {Zenodo},
+  version      = {v0.5.3},
+  doi          = {10.5281/zenodo.3541506},
+  url          = {https://doi.org/10.5281/zenodo.3541506}
+}
+
+@article{checkmate,
+  author = {Michel Lang},
+  title = {{checkmate: Fast Argument Checks for Defensive R Programming}},
+  year = {2017},
+  journal = {{The R Journal}},
+  doi = {10.32614/RJ-2017-028},
+  pages = {437--445},
+  volume = {9},
+  number = {1}
+}
+
+@Manual{paradox,
+  title = {paradox: Define and Work with Parameter Spaces for Complex Algorithms},
+  author = {Michel Lang and Bernd Bischl and Jakob Richter and Xudong Sun and Martin Binder},
+  year = {2024},
+  note = {R package version 1.0.1},
+  url = {https://CRAN.R-project.org/package=paradox}
+}
+
+@article{mlr3pipelines2021,
+  author = {Binder, Martin and Pfisterer, Florian and Lang, Michel and Schneider, Lennart and Kotthoff, Lars and Bischl, Bernd},
+  journal = {Journal of Machine Learning Research},
+  number = {184},
+  pages = {1--7},
+  title = {{mlr3pipelines - Flexible Machine Learning Pipelines in R}},
+  url = {http://jmlr.org/papers/v22/21-0281.html},
+  volume = {22},
+  year = {2021}
+}
+
+@article{Sonabend2021,
+  author = {Sonabend, Raphael and Kir{\'{a}}ly, Franz J. and Bender, Andreas and Bischl, Bernd and Lang, Michel},
+  doi = {10.1093/BIOINFORMATICS/BTAB039},
+  issn = {1367-4803},
+  journal = {Bioinformatics},
+  month = {sep},
+  number = {17},
+  pages = {2789--2791},
+  publisher = {Oxford Academic},
+  title = {{mlr3proba: an R package for machine learning in survival analysis}},
+  url = {https://academic.oup.com/bioinformatics/article/37/17/2789/6125361},
+  volume = {37},
+  year = {2021}
+}
+
+@incollection{benchlargescale,
+  author = "Sebastian Fischer and Michel Lang and Marc Becker",
+  title = "Large-Scale Benchmarking",
+  booktitle = "Applied Machine Learning Using {m}lr3 in {R}",
+  publisher = "CRC Press", year = "2024",
+  editor = "Bernd Bischl and Raphael Sonabend and Lars Kotthoff and Michel Lang",
+  url = "https://mlr3book.mlr-org.com/large-scale_benchmarking.html"
+}
+
+@article{wilkinson2016fair,
+  title={The FAIR Guiding Principles for scientific data management and stewardship},
+  author={Wilkinson, Mark D and Dumontier, Michel and Aalbersberg, IJsbrand Jan and Appleton, Gabrielle and Axton, Myles and Baak, Arie and Blomberg, Niklas and Boiten, Jan-Willem and da Silva Santos, Luiz Bonino and Bourne, Philip E and others},
+  journal={Scientific data},
+  volume={3},
+  number={1},
+  pages={1--9},
+  year={2016},
+  publisher={Nature Publishing Group}
+}
+
+@article{bischl2023,
+  archivePrefix = {arXiv},
+  arxivId = {2107.05847},
+  author = {Bischl, Bernd and Binder, Martin and Lang, Michel and Pielok, Tobias and Richter, Jakob and Coors, Stefan and Thomas, Janek and Ullmann, Theresa and Becker, Marc and Boulesteix, Anne Laure and Deng, Difan and Lindauer, Marius},
+  doi = {10.1002/WIDM.1484},
+  eprint = {2107.05847},
+  issn = {1942-4795},
+  journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
+  keywords = {automl,hyperparameter optimization,machine learning,model selection,tuning},
+  month = {mar},
+  number = {2},
+  pages = {e1484},
+  publisher = {John Wiley & Sons, Ltd},
+  title = {{Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges}},
+  url = {https://wires.onlinelibrary.wiley.com/doi/10.1002/widm.1484},
+  volume = {13},
+  year = {2023}
+}
+
+@Manual{mlr3learners,
+  title = {mlr3learners: Recommended Learners for 'mlr3'},
+  author = {Michel Lang and Quay Au and Stefan Coors and Patrick Schratz and Marc Becker},
+  year = {2024},
+  note = {R package version 0.9.0},
+  url = {https://CRAN.R-project.org/package=mlr3learners},
+}
+
+
+  @Manual{mlr3oml,
+    title = {mlr3oml: Connector Between 'mlr3' and 'OpenML'},
+    author = {Michel Lang and Sebastian Fischer},
+    year = {2024},
+    note = {R package version 0.10.0},
+    url = {https://CRAN.R-project.org/package=mlr3oml},
+  }
+
+  @article{vanschoren2014openml,
+  title={OpenML: networked science in machine learning},
+  author={Vanschoren, Joaquin and Van Rijn, Jan N and Bischl, Bernd and Torgo, Luis},
+  journal={ACM SIGKDD Explorations Newsletter},
+  volume={15},
+  number={2},
+  pages={49--60},
+  year={2014},
+  publisher={ACM New York, NY, USA}
+}
+
+
+@article{binder2020collecting,
+  title={Collecting empirical data about hyperparameters for data driven AutoML},
+  author={Binder, Martin and Pfisterer, Florian and Bischl, Bernd},
+  journal={Democratizing Machine Learning Contributions in AutoML and Fairness},
+  pages={93},
+  year={2020}
+}
+
+@Manual{mlr3batchmark,
+  title = {mlr3batchmark: Batch Experiments for 'mlr3'},
+  author = {Marc Becker and Michel Lang},
+  year = {2024},
+  note = {R package version 0.2.0},
+  url = {https://CRAN.R-project.org/package=mlr3batchmark},
+}
+
+@article{bischl2016mlr,
+  title={mlr: Machine Learning in R},
+  author={Bischl, Bernd and Lang, Michel and Kotthoff, Lars and Schiffner, Julia and Richter, Jakob and Studerus, Erich and Casalicchio, Giuseppe and Jones, Zachary M},
+  journal={Journal of Machine Learning Research},
+  volume={17},
+  number={170},
+  pages={1--5},
+  year={2016}
+}
+
+
+@article{lang2017batchtools,
+  title={batchtools: Tools for R to work on batch systems},
+  author={Lang, Michel and Bischl, Bernd and Surmann, Dirk},
+  journal={Journal of Open Source Software},
+  volume={2},
+  number={10},
+  pages={135},
+  year={2017}
+}
+
+
+  @Manual{mlr3torch,
+    title = {mlr3torch: Deep Learning with 'mlr3'},
+    author = {Sebastian Fischer and Martin Binder},
+    year = {2025},
+    note = {R package version 0.2.1},
+    url = {https://CRAN.R-project.org/package=mlr3torch},
+  }