TOSI: Two-Directional Simultaneous Inference for High-Dimensional Models

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>.

Version:	0.3.0
Depends:	R (≥ 4.0.0)
Imports:	MASS, hdi, scalreg, glmnet
Published:	2023-01-26
DOI:	10.32614/CRAN.package.TOSI
Author:	Wei Liu [aut, cre], Huazhen Lin [aut]
Maintainer:	Wei Liu <weiliu at smail.swufe.edu.cn>
BugReports:	https://github.com/feiyoung/TOSI/issues
License:	GPL-2 | GPL-3 [expanded from: GPL]
URL:	https://github.com/feiyoung/TOSI
NeedsCompilation:	no
Materials:	README
CRAN checks:	TOSI results

Documentation:

Reference manual:

TOSI.pdf

Downloads:

Package source:	TOSI_0.3.0.tar.gz
Windows binaries:	r-devel: TOSI_0.3.0.zip, r-release: TOSI_0.3.0.zip, r-oldrel: TOSI_0.3.0.zip
macOS binaries:	r-release (arm64): TOSI_0.3.0.tgz, r-oldrel (arm64): TOSI_0.3.0.tgz, r-release (x86_64): TOSI_0.3.0.tgz, r-oldrel (x86_64): TOSI_0.3.0.tgz
Old sources:	TOSI archive

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