Package: SoftBart 1.0.3

Antonio R. Linero

SoftBart: Implements the SoftBart Algorithm

Implements the SoftBart model of described by Linero and Yang (2018) <doi:10.1111/rssb.12293>, with the optional use of a sparsity-inducing prior to allow for variable selection. For usability, the package maintains the same style as the 'BayesTree' package.

Authors:Antonio R. Linero [aut, cre]

SoftBart_1.0.3.tar.gz
SoftBart_1.0.3.tar.gz(r-4.7-arm64)SoftBart_1.0.3.tar.gz(r-4.7-x86_64)SoftBart_1.0.3.tar.gz(r-4.6-arm64)SoftBart_1.0.3.tar.gz(r-4.6-x86_64)
SoftBart_1.0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SoftBart/json (API)

# Install 'SoftBart' in R:

install.packages('SoftBart', 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

3.56 score 1 packages 122 scripts 785 downloads 18 exports 81 dependencies

Last updated from:1483d495e1. Checks:6 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	185
linux-devel-x86_64	OK	178
source / vignettes	OK	245
linux-release-arm64	OK	175
linux-release-x86_64	OK	164
wasm-release	OK	668

Exports:contr.ltfr gsoftbart_regression Hypers MakeForest Opts partial_dependence_probit partial_dependence_regression pdsoftbart posterior_probs predict.softbart_probit predict.softbart_regression preprocess_df quantile_normalize_bart rmse softbart softbart_probit softbart_regression vc_softbart_regression

Dependencies:caret class cli clock codetools cpp11 crayon data.table diagram digest dplyr e1071 farver foreach future future.apply generics ggplot2 glmnet globals glue gower gtable hardhat hms ipred isoband iterators KernSmooth labeling lattice lava lifecycle listenv lubridate magrittr MASS Matrix ModelMetrics nlme nnet numDeriv parallelly pillar pkgconfig plyr prettyunits pROC prodlim progress progressr proxy purrr R6 RColorBrewer Rcpp RcppArmadillo RcppEigen recipes reshape2 rlang rpart S7 scales shape sparsevctrs SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate truncnorm tzdb utf8 vctrs viridisLite withr

SoftBartUsage

Rendered fromSoftBart-Vig.Rnwusingutils::Sweave

Last update: 2025-11-24
Started: 2022-10-29

Citation

Readme and manuals

Help Manual

Help page	Topics
Create a Full Set of Dummy Variables	contr.ltfr
General SoftBart Regression	gsoftbart_regression
Create a list of hyperparameter values	Hypers
Create an Rcpp_Forest Object	MakeForest
MCMC options for SoftBart	Opts
Partial Dependence Function for SoftBART Probit Regression	partial_dependence_probit
Partial Dependence Function for SoftBART Regression	partial_dependence_regression
Partial dependence plots for SoftBart	pdsoftbart
BART Posterior Inclusion Probabilities	posterior_probs
Predict for SoftBart Probit Regression	predict.softbart_probit
Predict for SoftBart Regression	predict.softbart_regression
Preprocess a dataset for use with SoftBart	preprocess_df
Quantile normalization for predictors	quantile_normalize_bart
Root mean squared error	rmse
Fits the SoftBart model	softbart
SoftBart Probit Regression	softbart_probit
SoftBart Regression	softbart_regression
SoftBart Varying Coefficient Regression	vc_softbart_regression

Package: SoftBart 1.0.3

SoftBart: Implements the SoftBart Algorithm

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)