rbbnp: A Bias Bound Approach to Non-Parametric Inference

A novel bias-bound approach for non-parametric inference is introduced, focusing on both density and conditional expectation estimation. It constructs valid confidence intervals that account for the presence of a non-negligible bias and thus make it possible to perform inference with optimal mean squared error minimizing bandwidths. This package is based on Schennach (2020) <doi:10.1093/restud/rdz065>.

Version: 0.1.0
Depends: R (≥ 3.5)
Imports: purrr, pracma, tidyr, dplyr, ggplot2, gridExtra
Published: 2024-02-01
Author: Xinyu DAI [aut, cre], Susanne M Schennach [aut]
Maintainer: Xinyu DAI <xinyu_dai at brown.edu>
License: GPL (≥ 3)
URL: https://doi.org/10.1093/restud/rdz065
NeedsCompilation: no
Materials: README
CRAN checks: rbbnp results

Documentation:

Reference manual: rbbnp.pdf

Downloads:

Package source: rbbnp_0.1.0.tar.gz
Windows binaries: r-prerel: rbbnp_0.1.0.zip, r-release: rbbnp_0.1.0.zip, r-oldrel: rbbnp_0.1.0.zip
macOS binaries: r-prerel (arm64): rbbnp_0.1.0.tgz, r-release (arm64): rbbnp_0.1.0.tgz, r-oldrel (arm64): rbbnp_0.1.0.tgz, r-prerel (x86_64): rbbnp_0.1.0.tgz, r-release (x86_64): rbbnp_0.1.0.tgz

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