hdm
High-Dimensional Metrics
Implementation of selected high-dimensional statistical and econometric methods for estimation and inference. Efficient estimators and uniformly valid confidence intervals for various low-dimensional causal/ structural parameters are provided which appear in high-dimensional approximately sparse models. Including functions for fitting heteroscedastic robust Lasso regressions with non-Gaussian errors and for instrumental variable (IV) and treatment effect estimation in a high-dimensional setting. Moreover, the methods enable valid post-selection inference and rely on a theoretically grounded, data-driven choice of the penalty. Chernozhukov, Hansen, Spindler (2016) doi:10.48550/arXiv.1603.01700.
- Version0.3.2
- R versionunknown
- LicenseMIT
- LicenseLICENSE
- Needs compilation?No
- hdm citation info
- Last release02/14/2024
Documentation
Team
Martin Spindler
Philipp Bach
Show author detailsRolesContributorVictor Chernozhukov
Show author detailsRolesAuthorChristian Hansen
Show author detailsRolesAuthor
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Last 30 days
This package has been downloaded 1,412 times in the last 30 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 84 times.
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Last 365 days
This package has been downloaded 18,666 times in the last 365 days. That's enough downloads to make it mildly famous in niche technical communities. A badge of honor! The day with the most downloads was Jul 20, 2024 with 218 downloads.
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Dependencies
- Imports5 packages
- Suggests7 packages
- Reverse Depends1 package
- Reverse Imports3 packages
- Reverse Suggests1 package