EAinference: Estimator Augmentation and Simulation-Based Inference

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: 0.2.1
Depends: R (≥ 3.2.3)
Imports: stats, graphics, msm, mvtnorm, parallel, limSolve, MASS, hdi, Rcpp
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, testthat
Published: 2017-10-13
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

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Reference manual: EAinference.pdf
Vignettes: Introduction to EAinference
Package source: EAinference_0.2.1.tar.gz
Windows binaries: r-devel: EAinference_0.2.1.zip, r-release: EAinference_0.2.1.zip, r-oldrel: EAinference_0.2.1.zip
OS X El Capitan binaries: r-release: EAinference_0.2.1.tgz
OS X Mavericks binaries: r-oldrel: EAinference_0.2.1.tgz
Old sources: EAinference archive

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