Package: ddml 0.3.0.9000
ddml: Double/Debiased Machine Learning
Estimate common causal parameters using double/debiased machine learning as proposed by Chernozhukov et al. (2018) <doi:10.1111/ectj.12097>. 'ddml' simplifies estimation based on (short-)stacking as discussed in Ahrens et al. (2024) <doi:10.1177/1536867X241233641>, which leverages multiple base learners to increase robustness to the underlying data generating process.
Authors:
ddml_0.3.0.9000.tar.gz
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ddml.pdf |ddml.html✨
ddml/json (API)
NEWS
| # Install 'ddml' in R: |
| install.packages('ddml', repos = c('https://thomaswiemann.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/thomaswiemann/ddml/issues
- AE98 - Random subsample from the data of Angrist & Evans (1991).
Last updated 6 days agofrom:6077ced0e6. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 17 2024 |
| R-4.5-win | OK | Nov 17 2024 |
| R-4.5-linux | OK | Nov 17 2024 |
| R-4.4-win | OK | Nov 17 2024 |
| R-4.4-mac | OK | Nov 17 2024 |
| R-4.3-win | OK | Nov 17 2024 |
| R-4.3-mac | OK | Nov 17 2024 |
Exports:crosspredcrossvalddml_ateddml_attddml_fplivddml_lateddml_plivddml_plmmdl_glmmdl_glmnetmdl_rangermdl_xgboostolsshortstacking
Dependencies:abindAERbackportsbootbroomcarcarDataclicodetoolscolorspacecowplotcpp11data.tableDerivdoBydplyrfansifarverforeachFormulagenericsggplot2glmnetgluegtableisobanditeratorsjsonlitelabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnnlsnumDerivpbkrtestpillarpkgconfigpurrrquadprogquantregR6rangerRColorBrewerRcppRcppEigenrlangsandwichscalesshapeSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithrxgboostzoo
