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DSFM (version 1.0.1)

Distributed Skew Factor Model Estimation Methods

Description

Provides a distributed framework for simulating and estimating skew factor models under various skewed and heavy-tailed distributions. The methods support distributed data generation, aggregation of local estimators, and evaluation of estimation performance via mean squared error, relative error, and sparsity measures. The distributed principal component (PC) estimators implemented in the package include 'IPC' (Independent Principal Component),'PPC' (Project Principal Component), 'SPC' (Sparse Principal Component), and other related distributed PC methods. The methodological background follows Guo G. (2023) .

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Version

Install

install.packages('DSFM')

Version

1.0.1

License

MIT + file LICENSE

Maintainer

Guangbao Guo

Last Published

December 1st, 2025

Functions in DSFM (1.0.1)

factor.tests

Factor Model Testing with Wald, GRS, PY tests and FDR control
calculate_errors

calculate_errors Function
wines

Piedmont wines data
SPC

The sparse principal component can obtain sparse solutions of the eigenmatrix to better explain the relationship between principal components and original variables.
SOPC

The sparse online principal component can not only process online data sets, but also obtain a sparse solution of online data sets.
DGulPC

Distributed Gul Principal Component Analysis
AirQuality

Air Quality Data Set (UCI)
Parkinsons_Features

Parkinson's Disease Voice Features Dataset
DFanPC

Distributed Fan Principal Component Analysis
DPC

Distributed Principal Component Analysis
SFM

The SFM function is to generate Skew Factor Models data.
DPPC

Distributed Probabilistic Principal Component Analysis
DSPC

Distributed Sparse Principal Component Analysis
DGaoPC

Distributed Gao Principal Component Analysis
Nutrimouse

Nutrimouse: Gene, Lipid and Grouping Data