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: <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

Documentation:

Reference manual: Sieve.pdf

Downloads:

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
Old sources: Sieve archive

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