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grf  

Generalized Random Forests
View on CRAN: Click here


Download and install grf package within the R console
Install from CRAN:
install.packages("grf")

Install from Github:
library("remotes")
install_github("cran/grf")

Install by package version:
library("remotes")
install_version("grf", "2.3.2")



Attach the package and use:
library("grf")
Maintained by
Erik Sverdrup
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-07-04
Latest Update: 2024-02-25
Description:
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.
How to cite:
Erik Sverdrup (2017). grf: Generalized Random Forests. R package version 2.3.2, https://cran.r-project.org/web/packages/grf
Previous versions and publish date:
0.9.2 (2017-07-04 14:56), 0.9.3 (2017-07-21 10:44), 0.9.4 (2017-11-27 09:49), 0.9.5 (2018-01-09 12:04), 0.9.6 (2018-04-14 16:50), 0.10.0 (2018-05-09 11:05), 0.10.1 (2018-09-24 18:30), 0.10.2 (2018-11-24 06:20), 0.10.3 (2019-05-27 07:00), 0.10.4 (2019-09-03 14:30), 1.0.0 (2019-12-02 00:00), 1.0.1 (2019-12-06 10:10), 1.1.0 (2020-03-12 07:30), 1.2.0 (2020-06-04 09:40), 2.0.0 (2021-06-22 06:10), 2.0.1 (2021-07-06 19:40), 2.0.2 (2021-07-14 18:00), 2.1.0 (2022-03-17 20:10), 2.2.0 (2022-08-06 11:00), 2.2.1 (2022-12-15 00:10), 2.3.0 (2023-05-11 01:00), 2.3.1 (2023-10-01 02:10)
Other packages that cited grf R package
View grf citation profile
Other R packages that grf depends, imports, suggests or enhances
Functions, R codes and Examples using the grf R package
Some associated functions: average_late . average_partial_effect . average_treatment_effect . best_linear_projection . boosted_regression_forest . boot_grf . causal_forest . causal_survival_forest . create_dot_body . custom_forest . estimate_rate . expected_survival . export_graphviz . generate_causal_data . generate_causal_survival_data . get_forest_weights . get_leaf_node . get_sample_weights . get_scores.causal_forest . get_scores.causal_survival_forest . get_scores.instrumental_forest . get_scores.multi_arm_causal_forest . get_scores . get_tree . grf-package . instrumental_forest . leaf_stats.causal_forest . leaf_stats.default . leaf_stats.instrumental_forest . leaf_stats.regression_forest . ll_regression_forest . lm_forest . merge_forests . multi_arm_causal_forest . multi_regression_forest . plot.grf_tree . plot.rank_average_treatment_effect . predict.boosted_regression_forest . predict.causal_forest . predict.causal_survival_forest . predict.instrumental_forest . predict.ll_regression_forest . predict.lm_forest . predict.multi_arm_causal_forest . predict.multi_regression_forest . predict.probability_forest . predict.quantile_forest . predict.regression_forest . predict.survival_forest . print.boosted_regression_forest . print.grf . print.grf_tree . print.rank_average_treatment_effect . print.tuning_output . probability_forest . quantile_forest . rank_average_treatment_effect.fit . rank_average_treatment_effect . regression_forest . split_frequencies . survival_forest . test_calibration . tune_causal_forest . tune_forest . tune_instrumental_forest . tune_ll_causal_forest . tune_ll_regression_forest . tune_regression_forest . variable_importance . 
Some associated R codes: RcppExports.R . analysis_tools.R . average_treatment_effect.R . boosted_regression_forest.R . causal_forest.R . causal_survival_forest.R . deprecated.R . dgps.R . forest_summary.R . get_scores.R . grf-package.R . input_utilities.R . instrumental_forest.R . ll_regression_forest.R . lm_forest.R . merge_forests.R . multi_arm_causal_forest.R . multi_regression_forest.R . plot.R . print.R . probability_forest.R . quantile_forest.R . rank_average_treatment.R . regression_forest.R . survival_forest.R . tune_forest.R . tune_ll_causal_forest.R . tune_ll_regression_forest.R .  Full grf package functions and examples
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