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.
Version: 2.4.0 Depends: R (≥ 3.5.0) Imports: DiceKriging, lmtest, Matrix, methods, Rcpp (≥ 0.12.15), sandwich (≥ 2.4-0) LinkingTo: Rcpp, RcppEigen Suggests: DiagrammeR, MASS, rdd, survival (≥ 3.2-8), testthat (≥ 3.0.4) Published: 2024-11-15 DOI: 10.32614/CRAN.package.grf 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 at monash.edu> BugReports: https://github.com/grf-labs/grf/issues License: GPL-3 URL: https://github.com/grf-labs/grf NeedsCompilation: yes SystemRequirements: GNU make In views: CausalInference, Econometrics, MachineLearning, MissingData CRAN checks: grf results Documentation: Downloads: Reverse dependencies: Reverse imports: aggTrees, causalQual, causalweight, cohetsurr, cramR, EpiForsk, evalITR, htetree, longsurr, OutcomeWeights, policytree, qeML, roseRF Reverse suggests: CRE, maq, rdss, targeted Linking:Please use the canonical form https://CRAN.R-project.org/package=grf to link to this page.
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