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SIHR: Statistical Inference in High Dimensional Regression
The goal of SIHR is to provide inference procedures in the high-dimensional setting for (1) linear functionals in generalized linear regression ('Cai et al.' (2019) <arXiv:1904.12891>, 'Guo et al.' (2020) <arXiv:2012.07133>, 'Cai et al.' (2021)), (2) conditional average treatment effects in generalized linear regression, (3) quadratic functionals in generalized linear regression ('Guo et al.' (2019) <arXiv:1909.01503>). (4) inner product in generalized linear regression (5) distance in generalized linear regression.
Getting started
Browse package contents
Package details |
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| Author | Prabrisha Rakshit, Zhenyu Wang, Tony Cai, Zijian Guo |
| Maintainer | Zijian Guo <zijguo@stat.rutgers.edu> |
| License | GPL-3 |
| Version | 2.0.1 |
| URL | https://github.com/prabrishar1/SIHR |
| Package repository | View on CRAN |
| Installation |
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