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 +1,198 @@
+@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}
+}
+