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Forest-based statistical estimation and inference. GRF provides non-parametric methods for heterogeneous treatment effects estimation (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables), as well as least-squares regression, quantile regression, and survival regression, all with support for missing covariates.
Package details |
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Author | Julie Tibshirani [aut], Susan Athey [aut], Rina Friedberg [ctb], Vitor Hadad [ctb], David Hirshberg [ctb], Luke Miner [ctb], Erik Sverdrup [aut, cre], Stefan Wager [aut], Marvin Wright [ctb] |
Maintainer | Erik Sverdrup <erik.sverdrup@monash.edu> |
License | GPL-3 |
Version | 2.3.2 |
URL | https://github.com/grf-labs/grf |
Package repository | View on CRAN |
Installation |
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