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
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:

Reference manual:	EAinference.pdf
Vignettes:	Introduction to EAinference

Downloads:

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 (arm64): EAinference_0.2.3.tgz, r-oldrel (arm64): EAinference_0.2.3.tgz, r-release (x86_64): EAinference_0.2.3.tgz, r-oldrel (x86_64): EAinference_0.2.3.tgz
Old sources:	EAinference archive

Linking:

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