tiltdens
Tilted and Data-Sharpened Nonparametric Density Estimation
High-order nonparametric density estimators built by perturbing a conventional kernel estimator, either by re-weighting the observations ("tilting") or by moving them ("data sharpening"). The perturbation is chosen so that the estimator inherits the fast convergence rate of an infinite-order kernel estimator, such as the sinc or trapezoidal flat-top estimator, while remaining a proper non-negative density without the oscillatory tails those estimators suffer from. Two criteria are provided: minimising the L2 distance to an infinite-order comparator, following Doosti and Hall (2016) <doi:10.1111/rssb.12112>, and minimising a cross-validation criterion that needs no comparator and is much faster, following Doosti, Hall and Mateu (2018) <doi:10.1016/j.jspi.2017.12.003>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling linux/jammy R-4.5 | tiltdens_0.1.1.tar.gz |
204.1 KiB |
0.1.1 |
rolling linux/noble R-4.5 | tiltdens_0.1.1.tar.gz |
204.1 KiB |
0.1.1 |
rolling source/ R- | tiltdens_0.1.1.tar.gz |
88.4 KiB |
0.1.1 |
latest linux/jammy R-4.5 | tiltdens_0.1.1.tar.gz |
204.1 KiB |
0.1.1 |
latest linux/noble R-4.5 | tiltdens_0.1.1.tar.gz |
204.1 KiB |
0.1.1 |
latest source/ R- | tiltdens_0.1.1.tar.gz |
88.4 KiB |
0.1.1 |
2026-04-23 source/ R- | tiltdens_0.1.1.tar.gz |
0 B |