Package: bcf 2.0.2

Jared S. Murray

bcf: Causal Inference using Bayesian Causal Forests

Causal inference for a binary treatment and continuous outcome using Bayesian Causal Forests. See Hahn, Murray and Carvalho (2020) <doi:10.1214/19-BA1195> for additional information. This implementation relies on code originally accompanying Pratola et. al. (2013) <arxiv:1309.1906>.

Authors:Jared S. Murray [aut, cre], P. Richard Hahn [aut], Carlos Carvalho [aut], Peter Mariani [ctb], Constance Delannoy [ctb], Mariel Finucane [ctb], Lauren V. Forrow [ctb], Drew Herren [ctb]

bcf_2.0.2.tar.gz
bcf_2.0.2.tar.gz(r-4.5-noble)bcf_2.0.2.tar.gz(r-4.4-noble)
bcf.pdf |bcf.html✨
bcf/json (API)
NEWS

# Install 'bcf' in R:

install.packages('bcf', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

5.10 score 4 stars 45 scripts 283 downloads 7 mentions 1 exports 74 dependencies

Last updated 8 months agofrom:ca73e732dc. Checks:OK: 2. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Oct 25 2024
R-4.5-linux-x86_64	OK	Oct 25 2024

Exports:bcf

Dependencies:backports base64enc bslib cachem checkmate cli cluster coda codetools colorspace data.table digest doParallel evaluate fansi farver fastmap fontawesome foreach foreign Formula fs ggplot2 glue gridExtra gtable highr Hmisc htmlTable htmltools htmlwidgets isoband iterators jquerylib jsonlite knitr labeling lattice lifecycle magrittr MASS Matrix matrixStats memoise mgcv mime munsell nlme nnet pillar pkgconfig R6 rappdirs RColorBrewer Rcpp RcppArmadillo RcppParallel rlang rmarkdown rpart rstudioapi sass scales stringi stringr tibble tinytex utf8 vctrs viridis viridisLite withr xfun yaml

A Simple Example

Rendered fromE01_simple-example.Rmdusingknitr::rmarkdownon Oct 25 2024.

Last update: 2024-02-28
Started: 2024-02-28

Prediction using BCF

Rendered fromE02_predict-example.Rmdusingknitr::rmarkdownon Oct 25 2024.

Last update: 2024-02-28
Started: 2024-02-28

Help page	Topics
Fit Bayesian Causal Forests	bcf
Takes a fitted bcf object produced by bcf() along with serialized tree samples and produces predictions for a new set of covariate values	predict.bcf
Takes a fitted bcf object produced by bcf() and produces summary stats and MCMC diagnostics. This function is built using the coda package and meant to mimic output from rstan::print.stanfit(). It includes, for key parameters, posterior summary stats, effective sample sizes, and Gelman and Rubin's convergence diagnostics. By default, those parameters are: sigma (the error standard deviation when the weights are all equal), tau_bar (the estimated sample average treatment effect), mu_bar (the average outcome under control/z=0 across all observations in the sample), and yhat_bat (the average outcome under the realized treatment assignment across all observations in the sample).	summary.bcf

Package: bcf 2.0.2

bcf: Causal Inference using Bayesian Causal Forests

A Simple Example

Prediction using BCF

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)