Package: grf 2.4.0
Erik Sverdrup
grf: Generalized Random Forests
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.
Authors:
grf_2.4.0.tar.gz
grf_2.4.0.tar.gz(r-4.5-noble)grf_2.4.0.tar.gz(r-4.4-noble)
grf_2.4.0.tgz(r-4.4-emscripten)grf_2.4.0.tgz(r-4.3-emscripten)
grf.pdf |grf.html✨
grf/json (API)
# Install 'grf' in R: |
install.packages('grf', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/grf-labs/grf/issues
Last updated 10 days agofrom:2f6b371daa. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 25 2024 |
R-4.5-linux-x86_64 | OK | Nov 25 2024 |
Exports:average_treatment_effectbest_linear_projectionboosted_regression_forestcausal_forestcausal_survival_forestgenerate_causal_datagenerate_causal_survival_dataget_forest_weightsget_leaf_nodeget_scoresget_treegrf_optionsinstrumental_forestll_regression_forestlm_forestmerge_forestsmulti_arm_causal_forestmulti_regression_forestprobability_forestquantile_forestrank_average_treatment_effectrank_average_treatment_effect.fitregression_forestsplit_frequenciessurvival_foresttest_calibrationvariable_importance
Dependencies:DiceKriginglatticelmtestMatrixRcppRcppEigensandwichzoo