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EAinference  

Estimator Augmentation and Simulation-Based Inference
View on CRAN: Click here


Download and install EAinference package within the R console
Install from CRAN:
install.packages("EAinference")

Install from Github:
library("remotes")
install_github("cran/EAinference")

Install by package version:
library("remotes")
install_version("EAinference", "0.2.3")



Attach the package and use:
library("EAinference")
Maintained by
Seunghyun Min
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-09-16
Latest Update: 2017-12-02
Description:
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.
How to cite:
Seunghyun Min (2017). EAinference: Estimator Augmentation and Simulation-Based Inference. R package version 0.2.3, https://cran.r-project.org/web/packages/EAinference. Accessed 21 Nov. 2024.
Previous versions and publish date:
0.2.0 (2017-09-16 23:57), 0.2.1 (2017-10-14 01:00), 0.2.2 (2017-11-13 11:14)
Other packages that cited EAinference R package
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Other R packages that EAinference depends, imports, suggests or enhances
Complete documentation for EAinference
Functions, R codes and Examples using the EAinference R package
Some associated functions: MHLS . PB.CI . PBsampler . cv.lasso . hdIS . lassoFit . plot.MHLS . postInference.MHLS . print.MHLS . summary.MHLS . 
Some associated R codes: MHInference.R . MHLS.R . PBsampler.R . RcppExports.R . cvlasso.R . grlasso.R . hdIS.R . utility.R .  Full EAinference package functions and examples
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