seunghyunmin/EAlasso: Estimator Augmentation and Simulation-Based Inference
Version 0.2.5

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.

Getting started

Package details

MaintainerSeunghyun Min <[email protected]>
LicenseGPL (>=2)
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
seunghyunmin/EAlasso documentation built on Feb. 12, 2018, 4:31 a.m.