This package is considered a duplicate. The official version of this package is found at:https://jaredsmurray.r-universe.dev/bcf

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.zip(r-4.7-x86_64)bcf_2.0.2.zip(r-4.7-arm64)bcf_2.0.2.zip(r-4.6-x86_64)bcf_2.0.2.zip(r-4.6-arm64)
bcf_2.0.2.tar.gz(r-4.7-arm64)bcf_2.0.2.tar.gz(r-4.7-x86_64)bcf_2.0.2.tar.gz(r-4.6-arm64)bcf_2.0.2.tar.gz(r-4.6-x86_64)
bcf_2.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
bcf/json (API)

# 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

On CRAN:

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

openblas cpp

4.92 score 4 stars 1 packages 577 downloads 7 mentions 1 exports 65 dependencies

Last updated from:ca73e732dc. Checks:10 OK. Indexed: no.

Target	Result	Time
linux-devel-arm64	OK	140
linux-devel-x86_64	OK	150
source / vignettes	OK	286
linux-release-arm64	OK	139
linux-release-x86_64	OK	164
windows-devel-arm64	OK	320
windows-devel-x86_64	OK	196
windows-release-arm64	OK	240
windows-release-x86_64	OK	167
wasm-release	OK	151

Exports:bcf

Dependencies:backports base64enc bslib cachem checkmate cli cluster coda codetools colorspace cpp11 data.table digest doParallel evaluate 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 matrixStats memoise mime nnet R6 rappdirs RColorBrewer Rcpp RcppArmadillo RcppParallel rlang rmarkdown rpart rstudioapi S7 sass scales stringi stringr tinytex vctrs viridisLite withr xfun yaml

A Simple Example

Rendered fromE01_simple-example.Rmdusingknitr::rmarkdown

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

Prediction using BCF

Predicting using BCF | Comparison

Rendered fromE02_predict-example.Rmdusingknitr::rmarkdown

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

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)