{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/high-dimensional-metrics-in-r","title":"High-Dimensional Metrics in R","arxiv_id":"1603.01700","date":"2016-03-05","proceeding":null,"authors":["Victor Chernozhukov","Chris Hansen","Martin Spindler"],"abstract":"The package High-dimensional Metrics (\\Rpackage{hdm}) is an evolving\ncollection of statistical methods for estimation and quantification of\nuncertainty in high-dimensional approximately sparse models. It focuses on\nproviding confidence intervals and significance testing for (possibly many)\nlow-dimensional subcomponents of the high-dimensional parameter vector.\nEfficient estimators and uniformly valid confidence intervals for regression\ncoefficients on target variables (e.g., treatment or policy variable) in a\nhigh-dimensional approximately sparse regression model, for average treatment\neffect (ATE) and average treatment effect for the treated (ATET), as well for\nextensions of these parameters to the endogenous setting are provided. Theory\ngrounded, data-driven methods for selecting the penalization parameter in Lasso\nregressions under heteroscedastic and non-Gaussian errors are implemented.\nMoreover, joint/ simultaneous confidence intervals for regression coefficients\nof a high-dimensional sparse regression are implemented, including a joint\nsignificance test for Lasso regression. Data sets which have been used in the\nliterature and might be useful for classroom demonstration and for testing new\nestimators are included. \\R and the package \\Rpackage{hdm} are open-source\nsoftware projects and can be freely downloaded from CRAN:\n\\texttt{http://cran.r-project.org}.","url_abs":"http://arxiv.org/abs/1603.01700v2","url_pdf":"http://arxiv.org/pdf/1603.01700v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"high-dimensional-metrics-in-r","repo_url":"https://github.com/MCKnaus/dmlmt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"high-dimensional-metrics-in-r","repo_url":"https://github.com/MartinSpindler/hdm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"high-dimensional-metrics-in-r","repo_url":"https://github.com/PhilippBach/hdm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"high-dimensional-metrics-in-r","repo_url":"https://github.com/PhilippBach/hdm_prev","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"},{"task_slug":"regression-1","task_name":"regression"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}