Package: BATTS 0.3.0

BATTS: Boosting and Bayesian Additive Trees for Two-Sample Comparison

Implements boosting and Bayesian additive tree methods for two-sample comparison through density ratio estimation, following Awaya, Xu, and Ma (2025). The package supports both point estimation and uncertainty quantification via Bayesian additive tree ensembles.

Authors:Naoki Awaya [aut, cre], Yuliang Xu [aut], Li Ma [aut]

BATTS_0.3.0.tar.gz
BATTS_0.3.0.zip(r-4.7-x86_64)BATTS_0.3.0.zip(r-4.7-arm64)BATTS_0.3.0.zip(r-4.6-x86_64)BATTS_0.3.0.zip(r-4.6-arm64)BATTS_0.3.0.zip(r-4.5-x86_64)
BATTS_0.3.0.tgz(r-4.6-x86_64)BATTS_0.3.0.tgz(r-4.6-arm64)BATTS_0.3.0.tgz(r-4.5-x86_64)BATTS_0.3.0.tgz(r-4.5-arm64)
BATTS_0.3.0.tar.gz(r-4.7-arm64)BATTS_0.3.0.tar.gz(r-4.7-x86_64)BATTS_0.3.0.tar.gz(r-4.6-arm64)BATTS_0.3.0.tar.gz(r-4.6-x86_64)
BATTS_0.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
BATTS/json (API)

# Install 'BATTS' in R:
install.packages('BATTS', repos = c('https://nawaya040.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/nawaya040/batts/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

openblascpp

3.59 score 1 stars 11 scripts 3 exports 2 dependencies

Last updated from:9ade23e222. Checks:13 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE101
linux-devel-x86_64NOTE149
source / vignettesOK178
linux-release-arm64NOTE141
linux-release-x86_64NOTE108
macos-release-arm64NOTE170
macos-release-x86_64NOTE348
macos-oldrel-arm64NOTE250
macos-oldrel-x86_64NOTE447
windows-devel-arm64NOTE369
windows-devel-x86_64NOTE234
windows-release-arm64NOTE359
windows-release-x86_64NOTE170
windows-oldrel-x86_64NOTE179
wasm-releaseOK115

Exports:battsbootseval_balance_weight

Dependencies:RcppRcppArmadillo