EAinference (0.2.3)

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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.

Maintainer: Seunghyun Min
Author(s): Seunghyun Min [aut, cre], Qing Zhou [aut]

License: GPL (>= 2)

Uses: hdi, limSolve, MASS, msm, mvtnorm, Rcpp, testthat, knitr, rmarkdown

Released over 1 year ago.

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