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-devel: rbbnp_0.1.0.zip, r-release: rbbnp_0.1.0.zip, r-oldrel: rbbnp_0.1.0.zip
macOS binaries:	r-release (arm64): rbbnp_0.1.0.tgz, r-oldrel (arm64): rbbnp_0.1.0.tgz, r-release (x86_64): rbbnp_0.1.0.tgz, r-oldrel (x86_64): rbbnp_0.1.0.tgz

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