Other packages > Find by keyword >

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", "2.5.0")



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: 2024-11-15
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 2.5.0, https://cran.r-project.org/web/packages/grf. Accessed 18 Sep. 2026.
Previous versions and publish date:
(2026-07-09 07:45), 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), 2.3.2 (2024-02-25 08:30), 2.4.0 (2024-11-15 11:10), 2.5.0 (2025-10-09 07:10), 2.6.0 (2026-03-03 08:50)
Other packages that cited grf R package
View grf citation profile
Other R packages that grf depends, imports, suggests or enhances
Complete documentation for grf
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
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

downlit  
Syntax Highlighting and Automatic Linking
Syntax highlighting of R code, specifically designed for the needs of 'RMarkdown' packages like 'pk ...
Download / Learn more Package Citations See dependency  
hmeasure  
The H-Measure and Other Scalar Classification Performance Metrics
Classification performance metrics that are derived from the ROC curve of a classifier. The package ...
Download / Learn more Package Citations See dependency  
injectoR  
R Dependency Injection
R dependency injection framework. Dependency injection allows a program design to follow the depend ...
Download / Learn more Package Citations See dependency  
r2resize  
In-Text Resize for Images, Tables and Fancy Resize Containers in 'shiny', 'rmarkdown' and 'quarto' Documents
Automatic resizing toolbar for containers, images and tables. Various resizable or expandable contai ...
Download / Learn more Package Citations See dependency  
eyelinker  
Import ASC Files from EyeLink Eye Trackers
Imports plain-text ASC data files from EyeLink eye trackers into (relatively) tidy data frames for ...
Download / Learn more Package Citations See dependency  
data360r  
Wrapper for 'TCdata360' and 'Govdata360' API
Makes it easy to engage with the Application Program Interface (API) of the 'TCdata360' and 'Govdat ...
Download / Learn more Package Citations See dependency  

28,565

R Packages

239,283

Dependencies

75,677

Author Associations

28,566

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA