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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 |
| Reference manual: | rbbnp.pdf |
| 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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