EAinference
Estimator Augmentation and Simulation-Based Inference
Estimator augmentation methods for statistical inference on high-dimensional data, as described in Zhou, Q. (2014) <doi:10.48550/arXiv.1401.4425> 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.
- Version0.2.3
- R version≥ 3.2.3
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?Yes
- Last release12/02/2017
Documentation
Team
Seunghyun Min
Qing Zhou
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Insights
Last 30 days
This package has been downloaded 174 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 5 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 2,197 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jul 24, 2024 with 28 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports6 packages
- Suggests3 packages
- Linking To2 packages