TOSI
0.3.0Two-Directional Simultaneous Inference for High-Dimensional Models
Overview
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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- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 87%
- Documented parameters
- 98%
- Return-value docs
- 100%
- References docs
- 53%
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People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- archivedRemoved from CRAN2026-07-10requires archived package 'scalreg'
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- 0.3.02023-01-26 · diff ↗
- 0.2.02023-01-24
- RR 4.2.0 released · 2022-04-22
Package metadata
- Total releases
- 2
- License
- GPL
- Minimum R
- ≥ 4.0.0
- Download size
- 13 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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