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IROmiss (version 1.0.2)

Imputation Regularized Optimization Algorithm

Description

Missing data are frequently encountered in high-dimensional data analysis, but they are usually difficult to deal with using standard algorithms, such as the EM algorithm and its variants. This package provides a general algorithm, the so-called Imputation Regularized Optimization (IRO) algorithm, for high-dimensional missing data problems. You can refer to Liang, F., Jia, B., Xue, J., Li, Q. and Luo, Y. (2018) at for detail.

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Version

Install

install.packages('IROmiss')

Monthly Downloads

14

Version

1.0.2

License

GPL-2

Maintainer

Bochao Jia

Last Published

February 19th, 2020

Functions in IROmiss (1.0.2)

IROmiss-package

Imputation Regularized Optimization Algorithm
yeast

Example dataset for learning Gaussian Graphical Models by the IRO Algorithm
SimGraDat

Simulate Incomplete Data for Gaussian Graphical Models
RCLM

Fit Random Coefficient Linear Models
SimRegDat

Simulate Incomplete Data for High-Dimensional Linear Regression.
SimRCLM

Simulate Dataset for Random Coefficient Linear Models
GraphIRO

Learning high-dimensional Gaussian Graphical Models with Missing Observations.
RCDat

A simulated dataset for random coefficient models.
RegICRO

Variable selection for high-dimensional Regression with Missing Data.
eye_norm

Example dataset for high-dimensional variable selection by the ICRO algorithm.
YeastIRO

Learning gene regulatory networks for Yeast Cell Expression Data.