Estimator Augmentation and Simulation-Based Inference

Estimator augmentation methods for statistical inference on high-dimensional data, as described in Zhou, Q. (2014) and Zhou, Q. and Min, S. (2017) . 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.


Reference manual

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0.2.3 by Seunghyun Min, 3 months ago

Browse source code at

Authors: Seunghyun Min [aut, cre], Qing Zhou [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports stats, graphics, msm, mvtnorm, parallel, limSolve, MASS, hdi, Rcpp

Suggests knitr, rmarkdown, testthat

Linking to Rcpp, RcppArmadillo

See at CRAN