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:
Sieve_2.1.tar.gz
Sieve_2.1.tar.gz(r-4.5-noble)Sieve_2.1.tar.gz(r-4.4-noble)
Sieve_2.1.tgz(r-4.4-emscripten)Sieve_2.1.tgz(r-4.3-emscripten)
Sieve.pdf |Sieve.html✨
Sieve/json (API)
| # Install 'Sieve' in R: |
| install.packages('Sieve', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:0ca9b04664. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 13 2024 |
| R-4.5-linux-x86_64 | OK | Oct 13 2024 |
Exports:create_index_matrixGenSamplessieve_predictsieve_preprocesssieve_solversieve.sgd.predictsieve.sgd.preprocesssieve.sgd.solver
Dependencies:codetoolscombinatforeachglmnetiteratorslatticeMASSMatrixRcppRcppArmadilloRcppEigenshapesurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Nonparametric Estimation by the Method of Sieves | Sieve-package Sieve |
| Clean up the fitted model | clean_up_result |
| Create the index matrix for multivariate regression | create_index_matrix |
| Generate some simulation/testing samples with nonlinear truth. | GenSamples |
| Predict the outcome of interest for new samples | sieve_predict |
| Preprocess the original data for sieve estimation. | sieve_preprocess |
| Calculate the coefficients for the basis functions | sieve_solver |
| Sieve-SGD makes prediction with new predictors. | sieve.sgd.predict |
| Preprocess the original data for sieve-SGD estimation. | sieve.sgd.preprocess |
| Fit sieve-SGD estimators, using progressive validation for hyperparameter tuning. | sieve.sgd.solver |