:parenttoc: True Release Notes ============= .. tab-set:: .. tab-item:: Python .. dropdown:: DoubleML 0.10.1 :class-title: sd-bg-primary sd-font-weight-bold :open: - **Release highlight:** Multi-Period Difference-in-Differences for Repeated Cross Sections - Implementation via ``DoubleMLDIDMulti`` class `Py #330 `_ `Py #345 `_ - Extended User Guide and Example Gallery `Docs #243 `_ - Allow user defined bandwidth for ``RDFlex`` `Py #343 `_ - Maintenance package `Py #327 `_ `Py #336 `_ - Maintenance documentation `Docs #241 `_ `Docs #242 `_ `Docs #244 `_ `Docs #245 `_ `Docs #246 `_ .. dropdown:: DoubleML 0.10.0 :class-title: sd-bg-primary sd-font-weight-bold :open: - **Release highlight:** Multi-Period Difference-in-Differences for Panel Data - Implementation via ``DoubleMLDIDMulti`` class `Py #292 `_ `Py #315 `_ - New ``doubleml.data`` submodule including ``DoubleMLData`` and ``DoubleMLPanelData`` classes `Py #292 `_ - Extended User Guide and Example Gallery `Docs #224 `_ `Docs #233 `_ `Docs #237 `_ - Added Confidence sets which are robust to weak IVs: ``robust_confset()`` method for ``DoubleMLIIVM`` (added by `Ezequiel Smucler `_ and `David Masip `_) `Py #318 `_ `Docs #234 `_ - Update sensitivity operations to improve sensitivity bounds `Py #295 `_ - Improve ``DoubleMLAPO`` nuisance estimation and update weighted score elements. Added example to compare ``DoubleMLIRM`` and ``DoubleMLAPO``. `Py #295 `_ `Py #297 `_ `Docs #220 `_ - Updated variance aggregation over repetitions via confidence intervals `Py #324 `_ `Docs #236 `_ - Added a separate package citation using `CITATION.cff` `Py #321 `_ - Update package formatting, linting and add pre-commit hooks `Py #288 `_ `Py #289 `_ `Py #294 `_ `Py #316 `_ - Maintenance package `Py #287 `_ `Py #288 `_ `Py #291 `_ `Py #319 `_ - Maintenance documentation `Docs #211 `_ `Docs #213 `_ `Docs #214 `_ `Docs #215 `_ `Docs #216 `_ `Docs #217 `_ `Docs #218 `_ `Docs #219 `_ `Docs #221 `_ `Docs #225 `_ `Docs #227 `_ `Docs #228 `_ `Docs #229 `_ `Docs #230 `_ `Docs #232 `_ `Docs #238 `_ `Docs #239 `_ .. dropdown:: DoubleML 0.9.3 :class-title: sd-bg-primary sd-font-weight-bold - Fix / adapted unit tests which failed in the release of 0.9.2 to conda-forge `Docs #208 `_ .. dropdown:: DoubleML 0.9.2 :class-title: sd-bg-primary sd-font-weight-bold - Make `rdrobust` optional for conda. Create `pyproject.toml` and remove `setup.py` for packaging `Py #285 `_ `Py #286 `_ - Maintenance package `Py #284 `_ - Maintenance documentation `Docs #205 `_ `Docs #206 `_ `Docs #207 `_ .. dropdown:: DoubleML 0.9.1 :class-title: sd-bg-primary sd-font-weight-bold - **Release highlight:** Regression Discontinuity Designs with Flexible Covariate Adjustment via ``RDFlex`` class (in cooperation with `Claudia Noack `_ and `Tomasz Olma `_; see `their paper `_) `Py #276 `_ - Add ``cov_type=HC0`` and enable key-worded arguments to ``DoubleMLBLP`` `Py #270 `_ `Py #271 `_ - Update User Guide and Example Gallery `Docs #204 `_ - Add AutoML example for tuning DoubleML estimators `Docs #199 `_ - Maintenance package `Py #268 `_ `Py #278 `_ `Py #279 `_ `Py #281 `_ `Py #282 `_ - Maintenance documentation `Docs #201 `_ `Docs #203 `_ .. dropdown:: DoubleML 0.9.0 :class-title: sd-bg-primary sd-font-weight-bold - **Release highlight:** Average potential outcomes for multiple discrete treatments via ``DoubleMLAPO`` and ``DoubleMLAPOS`` classes (proposed by `Apoorva Lal `_) `Py #245 `_ `Py #250 `_ - Update User Guide and Example Gallery `Docs #188 `_ `Docs #195 `_ - Add sensitivity analysis to ``DoubleMLFramework`` `Py #249 `_ - Maintenance package `Py #264 `_ `Py #265 `_ `Py #266 `_ - Maintenance documentation `Docs #182 `_ `Docs #184 `_ `Docs #186 `_ `Docs #193 `_ `Docs #194 `_ `Docs #196 `_ `Docs #197 `_ .. dropdown:: DoubleML 0.8.2 :class-title: sd-bg-primary sd-font-weight-bold - **API Update**: Change nuisance evaluation for classifiers. The corresponding properties are renamed ``nuisance_loss`` instead of ``rmses``. `Py #254 `_ `Docs #184 `_ - Add new example on sensitivity analysis `Docs #190 `_ - Add a new example on DiD with DoubleML in R `Docs #178 `_ - Enable ``set_sample_splitting`` for cluster data `Py #255 `_ - Update the ``make_confounded_irm_data`` data generating process `Py #263 `_ - Maintenance package `Py #264 `_ - Maintenance documentation `Docs #177 `_ `Docs #180 `_ `Docs #181 `_ `Docs #187 `_ `Docs #189 `_ .. dropdown:: DoubleML 0.8.1 :class-title: sd-bg-primary sd-font-weight-bold - Increment package requirements and update workflows for python 3.9 (add tests for python 3.12) `Py #247 `_ `Docs #175 `_ - Additional example for ranking treatment effects (by `Apoorva Lal `_) `Docs #173 `_ `Docs #174 `_ - Maintenance documentation `Docs #172 `_ .. dropdown:: DoubleML 0.8.0 :class-title: sd-bg-primary sd-font-weight-bold - **Release highlight:** Sample-selections models as ``DoubleMLSMM`` class (by `Michaela Kecskésová `_) `Py #231 `_ `Py #235 `_ `Docs #171 `_ - **API change:** Remove options ``apply_crossfitting`` and ``dml_procedure`` from the ``DoubleML`` class `Py #227 `_ `Docs #166 `_ - Restructure the package to improve readability and maintainability `Py #225 `_ - Add a ``DoubleMLFramework`` class to combine multiple DoubleML models (aggregation of estimates, boostrap and CI-procedures) `Py #226 `_ `Docs #169 `_ - Enable the use of external predictions for short models in benchmarks (by `Lucien `_) `Py #238 `_ `Py #239 `_ - Add the ``gain_statistics`` to ``utils`` to sensitivity analysis `Py #229 `_ - Maintenance documentation `Docs #162 `_ `Docs #163 `_ `Docs #164 `_ `Docs #165 `_ `Docs #167 `_ `Docs #168 `_ - Maintenance package `Py #225 `_ `Py #229 `_ `Py #246 `_ .. dropdown:: DoubleML 0.7.1 :class-title: sd-bg-primary sd-font-weight-bold - **Release highlight:** Add weights to ``DoubleMLIRM`` class to extend sensitivity to GATEs etc. `Py #220 `_ `Py #229 `_ `Docs #155 `_ `Docs #161 `_ - Extend GATE and CATE estimation to the ``DoubleMLPLR`` class `Py #220 `_ `Docs #155 `_ - Enable the use of external predictions for ``DoubleML`` classes `Py #221 `_ `Docs #159 `_ - Implementing utility classes and functions (gain statistics and dummy learners) `Py #221 `_ `Py #222 `_ `Py #229 `_ `Docs #161 `_ - Extend example Gallery `Docs #153 `_ `Docs #158 `_ `Docs #161 `_ - Maintenance documentation `Docs #157 `_ `Docs #160 `_ - Maintenance package `Py #223 `_ `Py #224 `_ .. dropdown:: DoubleML 0.7.0 :class-title: sd-bg-primary sd-font-weight-bold - **Release highlight:** Benchmarking for Sensitivity Analysis (omitted variable bias) `Py #211 `_ - Policy tree estimation for the ``DoubleMLIRM`` class `Py #212 `_ - Extending sensitivity and policy tree documentation in User Guide and Example Gallery `Docs #148 `_ `Docs #150 `_ - The package requirements are set to python 3.8 or higher `Py #211 `_ - Maintenance documentation `Docs #149 `_ - Maintenance package `Py #213 `_ .. dropdown:: DoubleML 0.6.3 :class-title: sd-bg-primary sd-font-weight-bold - Fix install requirements for 0.6.2 `Py #208 `_ .. dropdown:: DoubleML 0.6.2 :class-title: sd-bg-primary sd-font-weight-bold - **Release highlight:** Sensitivity Analysis (omitted variable bias) for `Py #201 `_ - ``DoubleMLPLR`` - ``DoubleMLIRM`` - ``DoubleMLDID`` - ``DoubleMLDIDCS`` - Updated documentation `Docs #144 `_ `Docs #141 `_ - Extend the guide with sensitivity and add further examples `Docs #142 `_ - Maintenance package `Py #202 `_ `Py #206 `_ - Maintenance documentation `Docs #137 `_ `Docs #138 `_ `Docs #140 `_ `Docs #143 `_ `Docs #145 `_ `Docs #146 `_ .. dropdown:: DoubleML 0.6.1 :class-title: sd-bg-primary sd-font-weight-bold - **Release highlight:** Difference-in-differences models for ATTE estimation `Py #200 `_ `Py #194 `_ - Panel data ``DoubleMLDID`` - Repeated cross sections ``DoubleMLDIDCS`` - Add a potential time variable to ``DoubleMLData`` (until now only used in ``DoubleMLDIDCS``) `Py #200 `_ - Extend the guide in the documentation and add further examples `Docs #132 `_ `Docs #133 `_ `Docs #135 `_ - Maintenance `Py #199 `_ `Docs #134 `_ `Docs #136 `_ .. dropdown:: DoubleML 0.6.0 :class-title: sd-bg-primary sd-font-weight-bold - **Release highlight:** Heterogeneous treatment effects (GATE, CATE, Quantile effects, ...) - Add out-of-sample RMSE and targets for nuisance elements and implement nuisance estimation evaluation via ``evaluate_learners()``. `Py #182 `_ `Py #188 `_ - Implement ``gate()`` and ``cate()`` methods for ``DoubleMLIRM`` class. Both are based on the new ``DoubleMLBLP`` class. `Py #169 `_ - Implement different type of quantile models `Py #179 `_ - Potential quantiles (PQ) in class ``DoubleMLPQ`` - Local potential quantiles (LPQ) in class ``DoubleMLLPQ`` - Conditional value at risk (CVaR) in class ``DoubleMLCVAR`` - Quantile treatment effects (QTE) in class ``DoubleMLQTE`` - Extend clustering to nonlinear scores `Py #190 `_ - Add ``ipw_normalization`` option to ``DoubleMLIRM`` and ``DoubleMLIIVM`` `Py #186 `_ - Implement an abstract base class for data backends `Py #173 `_ - Extend the guide in the documentation and add further examples `Docs #116 `_ `Docs #125 `_ `Docs #126 `_ - Code refactorings, bug fixes, docu updates, unit test extensions and continuous integration `Py #183 `_ `Py #192 `_ `Py #195 `_ `Py #196 `_ - Change License to BSD 3-Clause `Py #198 `_ - Maintenance `Py #174 `_ `Py #178 `_ `Py #181 `_ .. dropdown:: DoubleML 0.5.2 :class-title: sd-bg-primary sd-font-weight-bold - Fix / adapted unit tests which failed in the release of 0.5.1 to conda-forge `Py #172 `_ .. dropdown:: DoubleML 0.5.1 :class-title: sd-bg-primary sd-font-weight-bold - Store estimated models for nuisance parameters `Py #159 `_ - Bug fix: Overwrite for tune method (introduced for depreciation warning) did not return the tune result `Py #160 `_ `Py #162 `_ - Maintenance `Py #166 `_ `Py #167 `_ `Py #168 `_ `Py #170 `_ .. dropdown:: DoubleML 0.5.0 :class-title: sd-bg-primary sd-font-weight-bold - Implement a new score function ``score = 'IV-type'`` for the PLIV model (for details see `Py #151 `_) |br| --> **API change** from ``DoubleMLPLIV(obj_dml_data, ml_g, ml_m, ml_r [, ...])`` to ``DoubleMLPLIV(obj_dml_data, ml_g, ml_m, ml_r, ml_g [, ...])`` - Adapt the nuisance estimation for the ``'IV-type'`` score for the PLR model (for details see `Py #151 `_) |br| --> **API change** from ``DoubleMLPLR(obj_dml_data, ml_g, ml_m [, ...])`` to ``DoubleMLPLR(obj_dml_data, ml_l, ml_m, ml_g [, ...])`` - Allow the usage of classifiers for binary outcome variables in the model classes IRM and IIVM `Py #134 `_ - **Published in JMLR: DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python** (citation info updated in `Py #138 `_) - Maintenance `Py #143 `_ `Py #148 `_ `Py #149 `_ `Py #152 `_ `Py #153 `_ .. dropdown:: DoubleML 0.4.1 :class-title: sd-bg-primary sd-font-weight-bold - We added `Python Contribution Guidelines `_, issue templates, a pull request template and a `Python discussion forum `_ to the Python package repository `Py #132 `_ - Code refactorings, docu updates, unit test extensions and continuous integration `Py #126 `_ `Py #127 `_ `Py #128 `_ `Py #130 `_ `Py #131 `_ .. dropdown:: DoubleML 0.4.0 :class-title: sd-bg-primary sd-font-weight-bold - **Release highlight:** Clustered standard errors for double machine learning models `Py #116 `_ - Improve exception handling for missings and infinite values in the confounders, predictions, etc. (fixes `Py #120 `_ by allowing null confounder values) `Py #122 `_ - Clean up dev requirements and use dev requirements on github actions `Py #121 `_ - Other updates `Py #123 `_ .. dropdown:: DoubleML 0.3.0 :class-title: sd-bg-primary sd-font-weight-bold - Always use the same bootstrap algorithm independent of ``dml1`` vs ``dml2`` and consistent with docu and paper `Py #101 `_ & `Py #102 `_ - Added an exception handling to assure that an IV variable is specified when using a PLIV or IIVM model `Py #107 `_ - Improve exception handling for externally provided sample splitting `Py #110 `_ - Minor update of the str representation of ``DoubleMLData`` objects `Py #112 `_ - Code refactorings and unit test extensions `Py #103 `_, `Py #105 `_, `Py #106 `_, `Py #111 `_ & `Py #113 `_ .. dropdown:: DoubleML 0.2.2 :class-title: sd-bg-primary sd-font-weight-bold - IIVM model: Added a subgroups option to adapt to cases with and without the subgroups of always-takers and never-takers (`Py #96 `_). - Add checks for the intersections of ``y_col``, ``d_cols``, ``x_cols``, ``z_cols`` (`Py #84 `_, `Py #97 `_). This also fixes `Py #83 `_ (with intersection between ``x_cols`` and ``d_cols`` a column could have been added multiple times to the covariate matrix). - Added checks and exception handling for duplicate entries in ``d_cols``, ``x_cols`` or ``z_cols`` (`Py #100 `_). - Check the datatype of ``data`` when initializing ``DoubleMLData`` objects. Also check for duplicate column names (`Py #100 `_). - Fix bug `Py #95 `_ in `Py #97 `_: It occurred when ``x_cols`` where inferred via setdiff and ``y_col`` was a string with multiple characters. - We updated the citation info to refer to the arXiv paper (`Py #98 `_): Bach, P., Chernozhukov, V., Kurz, M. S., and Spindler, M. (2021), DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python, `arXiv:2104.03220 `_. .. dropdown:: DoubleML 0.2.1 :class-title: sd-bg-primary sd-font-weight-bold - Provide an option to store & export the first-stage predictions `Py #91 `_ - Added the package logo to the doc .. dropdown:: DoubleML 0.2.0 :class-title: sd-bg-primary sd-font-weight-bold - Major extensions of the unit test framework which result in a coverage >98% (a summary is given in `Py #82 `_) - In the PLR one can now also specify classifiers for ``ml_m`` in case of a binary treatment variable with values 0 and 1 (see `Py #86 `_ for details) - The joint Python and R docu and user guide is now served to `https://docs.doubleml.org `_ from a separate repo `https://github.com/DoubleML/doubleml-docs `_ - Generate and upload a unit test coverage report to codecov `https://app.codecov.io/gh/DoubleML/doubleml-for-py `_ `Py #76 `_ - Run lint checks with flake8 `Py #78 `_, align code with PEP8 standards `Py #79 `_, activate code quality checks at codacy `Py #80 `_ - Refactoring (reduce code redundancy) of the code for tuning of the ML learners used for approximation the nuisance functions `Py #81 `_ - Minor updates, bug fixes and improvements of the exception handling (contained in `Py #82 `_ & `Py #89 `_) .. dropdown:: DoubleML 0.1.2 :class-title: sd-bg-primary sd-font-weight-bold - Fixed a compatibility issue with ``scikit-learn`` 0.24, which only affected some unit tests (`Py #70 `_, `Py #71 `_) - Added scheduled unit tests on github-action (three times a week) `Py #69 `_ - Split up estimation of nuisance functions and computation of score function components. Further introduced a private method ``_est_causal_pars_and_se()``, see `Py #72 `_. This is needed for the DoubleML-Serverless project: https://github.com/DoubleML/doubleml-serverless. .. dropdown:: DoubleML 0.1.1 :class-title: sd-bg-primary sd-font-weight-bold - Bug fix in the drawing of bootstrap weights for the multiple treatment case `Py #66 `_ (see also https://github.com/DoubleML/doubleml-for-r/pull/28) - Update install instructions as DoubleML is now listed on pypi - Prepare submission to conda-forge: Include LICENSE file in source distribution - Documentation is now served with HTTPS `https://docs.doubleml.org/ `_ .. dropdown:: DoubleML 0.1.0 :class-title: sd-bg-primary sd-font-weight-bold - Initial release - Development at `https://github.com/DoubleML/doubleml-for-py `_ - The Python package **DoubleML** provides an implementation of the double / debiased machine learning framework of `Chernozhukov et al. (2018) `_. - Implements double machine learning for four different models: - Partially linear regression models (PLR) in class ``DoubleMLPLR`` - Partially linear IV regression models (PLIV) in class ``DoubleMLPLIV`` - Interactive regression models (IRM) in class ``DoubleMLIRM`` - Interactive IV regression models (IIVM) in class ``DoubleMLIIVM`` - All model classes are inherited from an abstract base class ``DoubleML`` where the key elements of double machine learning are implemented. .. tab-item:: R .. dropdown:: DoubleML 1.0.2 :class-title: sd-bg-primary sd-font-weight-bold :open: - Add sample selection models, thanks to new contributor Petra Jasenakova `@petronelaj `_ `R #213 `_ `Docs #223 `_ - Maintenance including updates to GitHub workflows `R #205 `_ `R #220 `_ `Docs #226 `_ .. dropdown:: DoubleML 1.0.1 :class-title: sd-bg-primary sd-font-weight-bold - Maintenance (upcoming breaking changes from ``paradox`` package), thanks to new contributor Martin Binder `@mb706 `_ `R #195 `_ `R #198 `_ .. dropdown:: DoubleML 1.0.0 :class-title: sd-bg-primary sd-font-weight-bold - Update citation info to publication in Journal of Statistical Software, rename helper function and fix links and GH actions `R #191 `_ .. dropdown:: DoubleML 0.5.3 :class-title: sd-bg-primary sd-font-weight-bold - Add documentation for estimated models for nuisance parameters `R #181 `_ - New contributor `@SvenKlaassen `_ - Maintenance `R #179 `_ .. dropdown:: DoubleML 0.5.2 :class-title: sd-bg-primary sd-font-weight-bold - Store estimated models for nuisance parameters `R #169 `_ - New maintainer of the CRAN package DoubleML `@PhilippBach `_ - Maintenance `R #170 `_ `R #173 `_ `R #174 `_ `R #177 `_ `R #178 `_ .. dropdown:: DoubleML 0.5.1 :class-title: sd-bg-primary sd-font-weight-bold - Fix a CRAN issue (html checks) by regenerating ``.Rd``-files with the newest version of ``roxygen2``. `R #166 `_ `R #167 `_ `R #168 `_ .. dropdown:: DoubleML 0.5.0 :class-title: sd-bg-primary sd-font-weight-bold - Implement a new score function ``score = 'IV-type'`` for the PLIV model (for details see `R #161 `_) |br| --> **API change** from ``DoubleMLPLIV$new(obj_dml_data, ml_g, ml_m, ml_r [, ...])`` to ``DoubleMLPLIV$new(obj_dml_data, ml_g, ml_m, ml_r, ml_g [, ...])`` - Adapt the nuisance estimation for the ``'IV-type'`` score for the PLR model (for details see `R #161 `_) |br| --> **API change** from ``DoubleMLPLR$new(obj_dml_data, ml_g, ml_m [, ...])`` to ``DoubleMLPLR$new(obj_dml_data, ml_l, ml_m, ml_g [, ...])`` - Use ``task_type`` instead of ``learner_class`` to identify whether a learner is meant to regress or classify (this change makes it possible to easily integrate pipelines from ``mlr3pipelines`` as learner for the nuisance functions) `R #141 `_ - Add `R Contribution Guidelines `_, issue templates, a pull request template and a `R discussion forum `_ to the R package repository `R #142 `_ `R #146 `_ `R #147 `_ - Allow the usage of classifiers for binary outcome variables in the model classes IRM and IIVM `R #114 `_ - Bug fixes and maintenance `R #155 `_ `R #156 `_ `R #157 `_ `R #158 `_ `R #160 `_ `R #163 `_ .. dropdown:: DoubleML 0.4.1 :class-title: sd-bg-primary sd-font-weight-bold - Prevent usage of ``glmnet`` learner for unit testing as recommended by CRAN (failing tests on Solaris) `R #137 `_ - Prepare for the upcoming release of ``checkmate`` which is not backward compatible with our unit tests `R #134 `_ .. dropdown:: DoubleML 0.4.0 :class-title: sd-bg-primary sd-font-weight-bold - **Release highlight:** Clustered standard errors for double machine learning models `R #119 `_ - Apply styler as described in the wiki (https://github.com/DoubleML/doubleml-for-r/wiki/Style-Guidelines) and add a corresponding CI on github actions `R #120 `_ `R #122 `_ - Other refactoring, bug fixes and documentation updates `R #127 `_ `R #129 `_ `R #130 `_ `R #131 `_ `R #132 `_ `R #133 `_ .. dropdown:: DoubleML 0.3.1 :class-title: sd-bg-primary sd-font-weight-bold - Initialize all numeric matrices, vectors and arrays with the correct data type by using ``NA_real_`` instead of ``NA`` and replace a ``print()`` call with ``cat()`` `R #115 `_ .. dropdown:: DoubleML 0.3.0 :class-title: sd-bg-primary sd-font-weight-bold - Use active bindings in the R6 OOP implementation `R #106 `_ & `R #93 `_ - Fix the aggregation formula for standard errors from repeated cross-fitting `R #94 `_ & `R #95 `_ - Always use the same bootstrap algorithm independent of ``dml1`` vs ``dml2`` and consistent with docu and paper `R #98 `_ & `R #99 `_ - Initialize predictions with NA and make sure that there are no misleading entries in the evaluated score functions `R #96 `_ & `R #105 `_ - Avoid overriding learner parameters during tuning `R #83 `_ & `R #84 `_ - Fixes in the exception handling and extension of the unit tests for the score function choice `R #82 `_ - Prevent overwriting parameters from initialization when calling set_ml_nuisance_params `R #87 `_ & `R #89 `_ - Major refactoring and cleanup and extension of the unit test framework `R #101 `_ - Extension and reorganization of exception handling for ``DoubleMLData`` objects `R #63 `_ & `R #90 `_ - Introduce style guide and clean up code `R #80 `_ & `R #81 `_ - Adaption to be compatible with an API change in the next ``mlr3`` release `R #103 `_ - Run unit tests with mlr3 in dev version on github actions `R #104 `_ - Updated the citation info `R #78 `_, `R #79 `_ & `R #86 `_ - Added a short version of and a reference to the arXiv paper as vignette `R #110 `_ & `R #113 `_ - Prevent using the subclassed methods check_score and check_data when constructing DoubleML objects `R #107 `_ - Other refactoring and minor adaptions `R #91 `_, `R #92 `_, `R #102 `_ & `R #108 `_ .. dropdown:: DoubleML 0.2.1 :class-title: sd-bg-primary sd-font-weight-bold - Provide an option to store & export the first-stage predictions `R #74 `_ - Reduce and refine messaging to the console during estimation `R #72 `_ - Fix bug in IIVM model if the IV variable is not named ``z`` `R #75 `_ - Fix failing unit test `R #71 `_ - Added the package logo to the doc .. dropdown:: DoubleML 0.2.0 :class-title: sd-bg-primary sd-font-weight-bold - In the PLR one can now also specify classifiers for ``ml_m`` in case of a binary treatment variable with values 0 and 1 - Major refactoring of core-parts of the estimation and tuning of the ML estimators for the nuisance functions: All models now use central helper functions ``dml_cv_predict()`` and ``dml_tune()`` - Extensions to the unit test framework to improve upon test coverage - Added unit test coverage via codecov: `https://app.codecov.io/gh/DoubleML/doubleml-for-r `_ - Minor docu updates and adaptions: `R #58 `_, `R #61 `_ & `R #70 `_ .. dropdown:: DoubleML 0.1.2 :class-title: sd-bg-primary sd-font-weight-bold - Adapt calls to ``mlr3tuning`` due to a change in their API (since version 0.6.0): fixes `R #51 `_ - Add ``bbotk`` to suggests: fixes R CMD check note `R #47 `_ - Use ``doi{}`` command: fixes R CMD check note `R #54 `_ - Minor docu updates as ``DoubleML`` is now available on CRAN .. dropdown:: DoubleML 0.1.1 :class-title: sd-bg-primary sd-font-weight-bold - First release to CRAN `https://cran.r-project.org/package=DoubleML `_ - Clean up of imports - Continuous integration was extended by unit tests on github actions `https://github.com/DoubleML/doubleml-for-r/actions `_ .. dropdown:: DoubleML 0.1.0 :class-title: sd-bg-primary sd-font-weight-bold - Initial release - Development at `https://github.com/DoubleML/doubleml-for-r `_ - The R package **DoubleML** provides an implementation of the double / debiased machine learning framework of `Chernozhukov et al. (2018) `_. - Implements double machine learning for four different models: - Partially linear regression models (PLR) in class ``DoubleMLPLR`` - Partially linear IV regression models (PLIV) in class ``DoubleMLPLIV`` - Interactive regression models (IRM) in class ``DoubleMLIRM`` - Interactive IV regression models (IIVM) in class ``DoubleMLIIVM`` - All model classes are inherited from ``DoubleML`` where the key elements of double machine learning are implemented. .. |br| raw:: html