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TOSI

This package provides a general framework of two directional simultaneous inference(TOSI) 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 factor models.

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install.packages('TOSI')

Monthly Downloads

177

Version

0.3.0

License

GPL

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Maintainer

Wei Liu

Last Published

January 26th, 2023

Functions in TOSI (0.3.0)

RegMax

Data splitting-based two-stage maximum testing method for the regression coefficients in linear regression models
RegMin

Data splitting-based two-Stage minimum testing method for the regression coefficients in linear regression models.
gendata_Mean

Generate simulated data
bic.spfac

Modified BIC criteria for selecting penalty parameters
ccorFun

Evaluate the smallest canonical correlation for two set of variables
gsspFactorm

High Dimensional Sparse Factor Model
gendata_Reg

Generate simulated data
cv.spfac

Cross validation for selecting penalty parameters
FacRowMaxST

Data splitting-based two-stage maximum testing method for a group of loading vectors in factor models.
Factorm

Factor Analysis Model
MeanMax

Data splitting-based two-stage maximum mean testing method for the mean vector.
gendata_Fac

Generate simulated data
assessBsFun

Assess the performance of group-sparse loading estimate
MeanMin

Data splitting-based two-stage minimum mean testing method for the mean vector.
FacRowMinST

Data splitting-based two-stage minimum testing method for a group of loading vectors in factor models.