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nbpInference: Inference on Average Treatment Effects for Continuous Treatments

Conduct inference on the sample average treatment effect for a matched (observational) dataset with a continuous treatment. Equipped with calipered non-bipartite matching, bias-corrected sample average treatment effect estimation, and covariate-adjusted variance estimation. Matching, estimation, and inference methods are described in Frazier, Heng and Zhou (2024) <doi:10.48550/arXiv.2409.11701>.

Version: 1.0.3
Imports: nbpMatching, stats, Rdpack
Suggests: testthat (≥ 3.0.0)
Published: 2025-10-17
DOI: 10.32614/CRAN.package.nbpInference
Author: Anthony Frazier [aut, cre, cph], Siyu Heng [aut], Wen Zhou [aut]
Maintainer: Anthony Frazier <anthony.frazier at colostate.edu>
BugReports: https://github.com/AnthonyFrazierCSU/nbpInference/issues
License: GPL (≥ 3)
URL: https://github.com/AnthonyFrazierCSU/nbpInference
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: nbpInference results

Documentation:

Reference manual: nbpInference.html , nbpInference.pdf

Downloads:

Package source: nbpInference_1.0.3.tar.gz
Windows binaries: r-devel: nbpInference_1.0.3.zip, r-release: nbpInference_1.0.3.zip, r-oldrel: nbpInference_1.0.3.zip
macOS binaries: r-release (arm64): nbpInference_1.0.3.tgz, r-oldrel (arm64): nbpInference_1.0.3.tgz, r-release (x86_64): nbpInference_1.0.3.tgz, r-oldrel (x86_64): nbpInference_1.0.3.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.