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: | 0.3.0 |
| Depends: | R (≥ 3.5) |
| Imports: | purrr,pracma,tidyr,dplyr,ggplot2,gridExtra |
| Published: | 2025-04-30 |
| DOI: | 10.32614/CRAN.package.rbbnp |
| 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 |
| Citation: | rbbnp citation info |
| Materials: | README,NEWS |
| CRAN checks: | rbbnp results |
| Reference manual: | rbbnp.html ,rbbnp.pdf |
| Package source: | rbbnp_0.3.0.tar.gz |
| Windows binaries: | r-devel:rbbnp_0.3.0.zip, r-release:rbbnp_0.3.0.zip, r-oldrel:rbbnp_0.3.0.zip |
| macOS binaries: | r-release (arm64):rbbnp_0.3.0.tgz, r-oldrel (arm64):rbbnp_0.3.0.tgz, r-release (x86_64):rbbnp_0.3.0.tgz, r-oldrel (x86_64):rbbnp_0.3.0.tgz |
| Old sources: | rbbnp archive |
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