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.
|Author||Seunghyun Min [aut, cre], Qing Zhou [aut]|
|Maintainer||Seunghyun Min <firstname.lastname@example.org>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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