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TOSI

Two-Directional Simultaneous Inference for High-Dimensional Models

v0.3.0 · Jan 26, 2023 · GPL

Description

A general framework of two directional simultaneous inference is provided for high-dimensional as well as the fixed dimensional models with manifest variable or latent variable structure, such as high-dimensional mean models, high- dimensional sparse regression models, and high-dimensional latent factors models. It is making the simultaneous inference on a set of parameters from two directions, one is testing whether the estimated zero parameters indeed are zero and the other is testing whether there exists zero in the parameter set of non-zero. More details can be referred to Wei Liu, et al. (2022) <doi:10.48550/arXiv.2012.11100>.

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NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
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dependencies in R code

Namespace in Imports field not imported from: ‘hdi’
  All declared Imports should be used.
NOTE r-devel-linux-x86_64-fedora-gcc

dependencies in R code

Namespace in Imports field not imported from: ‘hdi’
  All declared Imports should be used.

Dependency Network

Dependencies Reverse dependencies MASS hdi scalreg glmnet TOSI

Version History

new 0.3.0 Mar 10, 2026