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 and model assumptions can be found in: <arXiv:2206.02994> <arXiv:2104.00846>.

Version: 2.0
Imports: Rcpp, combinat, glmnet, methods, MASS
LinkingTo: Rcpp, RcppArmadillo
Published: 2023-09-03
Author: Tianyu Zhang
Maintainer: Tianyu Zhang <tianyuz3 at>
License: GPL-2
NeedsCompilation: yes
Materials: README
CRAN checks: Sieve results


Reference manual: Sieve.pdf


Package source: Sieve_2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): Sieve_2.0.tgz, r-oldrel (arm64): Sieve_2.0.tgz, r-release (x86_64): Sieve_2.0.tgz, r-oldrel (x86_64): Sieve_2.0.tgz
Old sources: Sieve archive


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