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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.3.0 |
rolling linux/jammy R-4.5 | rbbnp_0.3.0.tar.gz |
327.4 KiB |
0.3.0 |
rolling linux/noble R-4.5 | rbbnp_0.3.0.tar.gz |
327.1 KiB |
0.3.0 |
rolling source/ R- | rbbnp_0.3.0.tar.gz |
256.3 KiB |
0.3.0 |
latest linux/jammy R-4.5 | rbbnp_0.3.0.tar.gz |
327.4 KiB |
0.3.0 |
latest linux/noble R-4.5 | rbbnp_0.3.0.tar.gz |
327.1 KiB |
0.3.0 |
latest source/ R- | rbbnp_0.3.0.tar.gz |
256.3 KiB |
0.3.0 |
2026-04-26 source/ R- | rbbnp_0.3.0.tar.gz |
256.3 KiB |
0.3.0 |
2026-04-23 source/ R- | rbbnp_0.3.0.tar.gz |
256.3 KiB |
0.3.0 |
2026-04-09 windows/windows R-4.5 | rbbnp_0.3.0.zip |
331.5 KiB |
0.2.0 |
2025-04-20 source/ R- | rbbnp_0.2.0.tar.gz |
251.7 KiB |