bartMachine: Bayesian Additive Regression Trees
An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization
R (>= 2.14.0), rJava (>= 0.8-4), car, randomForest, missForest

Adam Kapelner and Justin Bleich (R package), see COPYRIGHTS file for the authors of the java libraries

An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization.

Adam Kapelner and Justin Bleich (R package)
R (>= 2.14.0), rJava (>= 0.9-8), bartMachineJARs (>= 1.0),
car, randomForest, missForest

R (>= 2.14.0), rJava (>= 0.9-8), bartMachineJARs (>= 1.0),
randomForest, missForest

R (>= 2.14.0), rJava (>= 0.9-8), bartMachineJARs (>= 1.2.1),
randomForest, missForest

R (>= 2.14.0), rJava (>= 0.9-8), bartMachineJARs (>= 1.2),
randomForest, missForest

These are bartMachine's Java dependency libraries. Note: this package has no functionality of its own and should not be installed as a standalone package without bartMachine.

Bayesian Additive Regression Trees for Confounder Selection
person("Yeonghoon", "Yoo", , "yooyh.stat@gmail.com", role = c("aut", "cre"))
Fit Bayesian Regression Additive Trees (BART) models to
select relevant confounders among a large set of potential confounders
and to estimate average treatment effect. For more information, see
Kim et al. (2022) <doi:10.48550/arXiv.2203.11798>.
ggcharts, ggplot2, invgamma, MCMCpack, Rcpp, rlang, rootSolve,
stats

Bayesian Variable Selection with Shrinking and Diffusing Priors
Qingyan Xiang <qxiang@illinois.edu>, Naveen Narisetty <naveen@illinois.edu>
Provides a Bayesian variable selection approach using continuous spike and slab prior distributions. The prior choices here are motivated by the shrinking and diffusing priors studied in Narisetty & He (2014) <DOI:10.1214/14-AOS1207>.

Qingyan Xiang <qyxiang@bu.edu>, Naveen Narisetty <naveen@illinois.edu>

Convert Regression Between Base Function and 'rms' Package
c(
person("Jing","Zhang",role = c("aut", "cre"),email = "zj391120@163.com"),
person("Zhi", "Jin", role="aut")
)
We perform linear, logistic, and cox regression using the base functions lm(),
glm(), and coxph() in the R software and the 'survival' package. Likewise, we
can use ols(), lrm() and cph() from the 'rms' package for the same
functionality. Each of these two sets of commands has a different focus.
In many cases, we need to use both sets of commands in the same situation,
e.g. we need to filter the full subset model using AIC, and we need to build
a visualization graph for the final model. 'base.rms' package can help you to switch
between the two sets of commands easily.