Package: Sieve 2.1

Tianyu Zhang

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>.

Authors:Tianyu Zhang

Sieve_2.1.tar.gz
Sieve_2.1.tar.gz(r-4.7-arm64)Sieve_2.1.tar.gz(r-4.7-x86_64)Sieve_2.1.tar.gz(r-4.6-arm64)Sieve_2.1.tar.gz(r-4.6-x86_64)
Sieve_2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
Sieve/json (API)

# Install 'Sieve' in R:
install.packages('Sieve', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

2.30 score 40 scripts 196 downloads 8 exports 13 dependencies

Last updated from:0ca9b04664. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK148
linux-devel-x86_64OK140
source / vignettesOK176
linux-release-arm64OK206
linux-release-x86_64OK162
wasm-releaseOK138

Exports:create_index_matrixGenSamplessieve_predictsieve_preprocesssieve_solversieve.sgd.predictsieve.sgd.preprocesssieve.sgd.solver

Dependencies:codetoolscombinatforeachglmnetiteratorslatticeMASSMatrixRcppRcppArmadilloRcppEigenshapesurvival