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Merge pull request #191 from DoubleML/p-citation
Update Citation, increment version
2 parents ba452ab + ecd6ec6 commit dcedb8b

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.github/workflows/check_styler.yml

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GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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with:
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fetch-depth: 2
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.github/workflows/deploy_docu.yml

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runs-on: ubuntu-20.04
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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persist-credentials: false
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- name: Install SSH Client
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shell: Rscript {0}
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- name: Cache R packages
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uses: actions/cache@v3
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uses: actions/cache@v4
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with:
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path: ${{ env.R_LIBS_USER }}
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key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }}

.github/workflows/deploy_docu_dev.yml

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runs-on: ubuntu-20.04
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- name: Install SSH Client
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- name: Cache R packages
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uses: actions/cache@v3
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uses: actions/cache@v4
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with:
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path: ${{ env.R_LIBS_USER }}
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key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }}

.github/workflows/deploy_pkg.yml

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runs-on: ubuntu-20.04
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- uses: r-lib/actions/setup-r@v2
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with:
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shell: Rscript {0}
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- name: Cache R packages
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uses: actions/cache@v3
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uses: actions/cache@v4
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with:
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path: ${{ env.R_LIBS_USER }}
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key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }}
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run: |
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R CMD build .
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- uses: actions/upload-artifact@v3
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- uses: actions/upload-artifact@v4
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with:
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name: DoubleML-pkg
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path: DoubleML_*.tar.gz

.github/workflows/rcheck.yml

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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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fetch-depth: 2
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- name: Cache R packages
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if: runner.os != 'Windows'
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uses: actions/cache@v3
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uses: actions/cache@v4
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with:
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path: ${{ env.R_LIBS_USER }}
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key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-${{ matrix.config.mlr3 }}-1-${{ hashFiles('.github/depends.Rds') }}
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- name: Upload check results
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if: failure()
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uses: actions/upload-artifact@v3
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uses: actions/upload-artifact@v4
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with:
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name: ${{ runner.os }}-r${{ matrix.config.r }}-${{ matrix.config.mlr3 }}-results
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path: check

DESCRIPTION

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Package: DoubleML
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Type: Package
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Title: Double Machine Learning in R
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Version: 0.5.0.9000
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Version: 1.0.0
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Authors@R: c(
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person("Philipp", "Bach", email = "philipp.bach@uni-hamburg.de", role=c("aut", "cre")),
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person("Victor", "Chernozhukov", role="aut"),
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these models by machine learning methods and computation of the Neyman
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orthogonal score functions. 'DoubleML' is built on top of 'mlr3' and the
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'mlr3' ecosystem. The object-oriented implementation of 'DoubleML' based on
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the 'R6' package is very flexible.
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the 'R6' package is very flexible. More information available in the
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publication in the Journal of Statistical Software: <10.18637/jss.v108.i03>.
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License: MIT + file LICENSE
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URL: https://docs.doubleml.org/stable/index.html, https://github.com/DoubleML/doubleml-for-r/
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BugReports: https://github.com/DoubleML/doubleml-for-r/issues
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mlr3learners (>= 0.3.0),
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mlr3misc
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Roxygen: list(markdown = TRUE, r6 = TRUE)
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RoxygenNote: 7.2.3
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RoxygenNote: 7.3.1
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Suggests:
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knitr,
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rmarkdown,

DoubleML.Rproj

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BuildType: Package
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PackageUseDevtools: Yes
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PackageInstallArgs: --no-multiarch --with-keep.source
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PackageCheckArgs: --no-tests --run-donttest
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PackageCheckArgs: --as-cran --no-tests --run-donttest

R/datasets.R

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#' available at [https://academic.oup.com/ectj/article/21/1/C1/5056401#supplementary-data](https://academic.oup.com/ectj/article/21/1/C1/5056401#supplementary-data).
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#'
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#' The supplementary data of the study by Bilias (2000) is available at
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#' [http://qed.econ.queensu.ca/jae/2000-v15.6/bilias/](http://qed.econ.queensu.ca/jae/2000-v15.6/bilias/).
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#' [https://www.journaldata.zbw.eu/dataset/sequential-testing-of-duration-data-the-case-of-the-pennsylvania-reemployment-bonus-experiment](https://www.journaldata.zbw.eu/dataset/sequential-testing-of-duration-data-the-case-of-the-pennsylvania-reemployment-bonus-experiment).
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#'
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#'
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#' @references Bilias Y. (2000), Sequential Testing of Duration Data:
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#' @references Turrell, A. (2018), Econometrics in Python part I - Double
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#' machine learning, Markov Wanderer: A blog on economics, science, coding and
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#' data.
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#' [http://aeturrell.com/2018/02/10/econometrics-in-python-partI-ML/](http://aeturrell.com/2018/02/10/econometrics-in-python-partI-ML/).
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#' [https://aeturrell.com/blog/posts/econometrics-in-python-parti-ml/](https://aeturrell.com/blog/posts/econometrics-in-python-parti-ml/).
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#' The number of observations to simulate.

R/double_ml.R

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a = (1 - level) / 2
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a = c(a, 1 - a)
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pct = format.perc(a, 3)
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pct = format_perc(a, 3)
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fac = qnorm(a)
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ci = array(NA_real_,
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pct = format_perc(ab, 3)
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R/double_ml_plr.R

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#' in the [mlr3tuning](https://mlr3tuning.mlr-org.com/) package. For more
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#' information on tuning in [mlr3](https://mlr3.mlr-org.com/), we refer to
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#' the section on parameter tuning in the
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#' [mlr3 book](https://mlr3book.mlr-org.com/optimization.html#tuning).
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#' [mlr3 book](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html).
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#' @param param_set (named `list()`) \cr
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#' A named `list` with a parameter grid for each nuisance model/learner

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