EAinference: Estimator Augmentation and Simulation-Based Inference
Estimator augmentation methods for statistical inference on high-dimensional data,
as described in Zhou, Q. (2014) <doi:10.48550/arXiv.1401.4425>
and Zhou, Q. and Min, S. (2017) <doi:10.1214/17-EJS1309>.
It provides several simulation-based inference methods: (a) Gaussian and
wild multiplier bootstrap for lasso, group lasso, scaled lasso, scaled group
lasso and their de-biased estimators, (b) importance sampler for approximating
p-values in these methods, (c) Markov chain Monte Carlo lasso sampler with
applications in post-selection inference.
| Version: |
0.2.3 |
| Depends: |
R (≥ 3.2.3) |
| Imports: |
stats, graphics, msm, mvtnorm, parallel, limSolve, MASS, hdi, Rcpp |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
knitr, rmarkdown, testthat |
| Published: |
2017-12-02 |
| DOI: |
10.32614/CRAN.package.EAinference |
| Author: |
Seunghyun Min [aut, cre],
Qing Zhou [aut] |
| Maintainer: |
Seunghyun Min <seunghyun at ucla.edu> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| CRAN checks: |
EAinference results |
Documentation:
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