Sieve
Nonparametric Estimation by the Method of Sieves
Performs multivariate nonparametric regression/classification by the method of sieves (using orthogonal basis). The method is suitable for moderate high-dimensional features (dimension < 100). The l1-penalized sieve estimator, a nonparametric generalization of Lasso, is adaptive to the feature dimension with provable theoretical guarantees. We also include a nonparametric stochastic gradient descent estimator, Sieve-SGD, for online or large scale batch problems. Details of the methods can be found in: <arXiv:2206.02994> <arXiv:2104.00846><arXiv:2310.12140>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.1 |
rolling linux/jammy R-4.5 | Sieve_2.1.tar.gz |
156.6 KiB |
2.1 |
rolling linux/noble R-4.5 | Sieve_2.1.tar.gz |
158.8 KiB |
2.1 |
rolling source/ R- | Sieve_2.1.tar.gz |
31.0 KiB |
2.1 |
latest linux/jammy R-4.5 | Sieve_2.1.tar.gz |
156.6 KiB |
2.1 |
latest linux/noble R-4.5 | Sieve_2.1.tar.gz |
158.8 KiB |
2.1 |
latest source/ R- | Sieve_2.1.tar.gz |
31.0 KiB |
2.1 |
2026-04-26 source/ R- | Sieve_2.1.tar.gz |
31.0 KiB |
2.1 |
2026-04-23 source/ R- | Sieve_2.1.tar.gz |
31.0 KiB |
2.1 |
2026-04-09 windows/windows R-4.5 | Sieve_2.1.zip |
476.3 KiB |
2.1 |
2025-04-20 source/ R- | Sieve_2.1.tar.gz |
31.0 KiB |