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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: <doi:10.48550/arXiv.2206.02994> <doi:10.48550/arXiv.2104.00846><doi:10.48550/arXiv.2310.12140>.
| Version: | 2.1 |
| Imports: | Rcpp, combinat, glmnet, methods, MASS |
| LinkingTo: | Rcpp, RcppArmadillo |
| Published: | 2023-10-19 |
| Author: | Tianyu Zhang |
| Maintainer: | Tianyu Zhang <tianyuz3 at andrew.cmu.edu> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | Sieve results |
| Reference manual: | Sieve.pdf |
| Package source: | Sieve_2.1.tar.gz |
| Windows binaries: | r-devel: Sieve_2.1.zip, r-release: Sieve_2.1.zip, r-oldrel: Sieve_2.1.zip |
| macOS binaries: | r-release (arm64): Sieve_2.1.tgz, r-oldrel (arm64): Sieve_2.1.tgz, r-release (x86_64): Sieve_2.1.tgz, r-oldrel (x86_64): Sieve_2.1.tgz |
| Old sources: | Sieve archive |
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