Estimator augmentation methods for statistical inference on high-dimensional data, as described in Zhou, Q. (2014) <arXiv:1401.4425v2> 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 |
| 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 |
| Reference manual: | EAinference.pdf |
| Vignettes: |
Introduction to EAinference |
| Package source: | EAinference_0.2.3.tar.gz |
| Windows binaries: | r-devel: EAinference_0.2.3.zip, r-release: EAinference_0.2.3.zip, r-oldrel: EAinference_0.2.3.zip |
| macOS binaries: | r-release: EAinference_0.2.3.tgz, r-oldrel: EAinference_0.2.3.tgz |
| Old sources: | EAinference archive |
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